Digestive cancers are among the leading causes of cancer death in the world. However, the mechanisms of cancer development and progression are not fully understood. Accumulating evidence in recent years pointing to the bidirectional interactions between gut dysbiosis and the development of a specific type of gastrointestinal cancer is shedding light on the importance of this “unseen organ”—the microbiota. This review focuses on the local role of the gut microbiota imbalance in different digestive tract organs and annexes related to the carcinogenic mechanisms. Microbiota modulation, either by probiotic administration or by dietary changes, plays an important role in the future therapies of various digestive cancers. Full article

Listeria monocytogenes (L. monocytogenes) poses a significant threat to food safety due to its ability to cause severe human illness and its resistance to various antibiotics and environmental conditions. This study investigated the prevalence, serotype distribution, virulence gene profiles, and antimicrobial resistance patterns of L. monocytogenes in ready-to-eat (RTE) food products from Romania. A total of 8151 samples were analyzed, including various processed dairy, bovine, poultry, pork, and fish products. Bacterial isolation was conducted using the classical standard method, followed by confirmation through biochemical and molecular testing. Among the isolated strains, serotypes 1/2a, 1/2b, and 1/2c were identified, with a prevalence of 75% for serotype 1/2a. Additionally, virulence genes specific to listeriolysin O (hlyA) and regulatory factor A (prfA) were detected in all isolates. Antimicrobial susceptibility testing revealed varying resistance patterns among the L. monocytogenes strains. Trimethoprim-sulfamethoxazole and oxacillin showed the highest prevalence of resistance at 26.92% and 23.07%, respectively. However, all strains remained susceptible to ciprofloxacin, levofloxacin, and moxifloxacin. Notably, 23.07% of the isolates exhibited multidrug resistance, with the most common pattern being resistance to oxacillin, penicillin, and tetracycline. Analysis of antimicrobial resistance genes identified tetracycline resistance genes, particularly tet(C), tet(M), and tet(K), in a significant proportion of isolates. The presence of ampC and dfrD genes was also notable, indicating potential mechanisms of resistance. These results emphasize the necessity for ongoing surveillance of L. monocytogenes in RTE foods and emphasize the importance of thorough monitoring of antimicrobial resistance to guide public health strategies within the European Union. Full article

The present study involves the precise identification and safety evaluation of Enterococcus casseliflavus KB1733, previously identified using 16S rRNA analysis, through whole-genome sequencing, phenotypic analysis, and preclinical toxicity studies. Analyses based on the genome sequencing data confirm the identity of KB1733 as E. casseliflavus and show that the genes related to vancomycin resistance are only present on the chromosome, while no virulence factor genes are present on the chromosome or plasmid. Phenotypic analyses of antibiotic resistance and hemolytic activity also indicated no safety concerns. A bacterial reverse mutation test showed there was no increase in revertant colonies of heat-killed KB1733. An acute toxicity test employing heat-killed KB1733 at a dose of 2000 mg/kg body weight in rats resulted in no deaths and no weight gain or other abnormalities in the general condition of the animals, with renal depression foci and renal cysts only occurring at the same frequency as in the control. Taking the background data into consideration, the effects on the kidneys observed in the current study were not caused by KB1733. Our findings suggest that KB1733 is non-pathogenic to humans/animals, although further studies involving repeated oral toxicity tests and/or clinical tests are required. Full article

Worldwide, human respiratory syncytial virus (HRSV) is a major cause of severe infections of the lower respiratory system, affecting individuals of all ages. This study investigated the genetic variability of HRSV during the COVID-19 outbreak in Yaoundé; nasopharyngeal samples positive for HRSV were collected from different age groups between July 2020 and October 2021. A semi-nested RT-PCR was performed on the second hypervariable region of the G gene of detected HRSV, followed by sequencing and phylogenetic assessment. Throughout the study, 40 (37.7%) of the 106 HRSV-positive samples successfully underwent G-gene amplification. HRSV A and HRSV B co-circulated at rates of 47.5% and 52.5%, respectively. HRSV A clustered in the GA2.3.5 genetic lineage (ON1) and HRSV B clustered in the GB5.0.5a genetic lineage (BA9). Differences in circulating genotypes were observed between pre- and post-pandemic years for HRSV A. Predictions revealed potential N-glycosylation sites at positions 237-318 of HRSV A and positions 228-232-294 of HRSV B. This study reports the molecular epidemiology of HRSV in Cameroon during the COVID-19 pandemic. It describes the exclusive co-circulation of two genetic lineages. These findings highlight the importance of implementing comprehensive molecular surveillance to prevent the unexpected emergence of other diseases. Full article

Due to its variety of signs and symptoms, there have been numerous attempts to treat fibromyalgia (FM), but a cure has yet to be established. The aim of this study was to evaluate the effects of a complex kinetic therapy program and a combined physical modality program on pain and other common symptoms of FM. Patients and methods: A total of 78 female patients were included in this study; 39 subjects underwent a kinesiotherapy (KT) intervention (combining aerobic and Pilates exercises), and 39 participated in a physical modality (PM) program (including electrotherapy (TENS and low-laser therapy) and thermotherapy). Results: Regarding the parameter of pain assessment, kinesiotherapy demonstrated its superiority both during the treatment period and in the evaluation 3 months after therapy cessation. Both in terms of patient-reported pain (inter-group comparisons: p = 0.000 at T3) and the examination of tender points (inter-group comparisons: p = 0.000 at T3), as well as the algometric assessment, pain was alleviated by the two forms of applied kinetic therapy. The observed functional impairment was statistically significantly influenced (p = 0.001) at the end of the kinetic program application, while for the perceived functional impairment, neither therapy proved superiority over the other at any point of evaluation (inter-group comparisons: p = 0.715 at T3). Regarding the influence of the emotional consequences implied by fibromyalgia, neither the forms of kinesiotherapy nor the chosen physical modalities proved superiority at any point of evaluation (HAQ anxiety inter-group comparisons: p = 0.000 at T3). In conclusion, even though kinesiotherapy had superior influences on fibromyalgia pain in the studied group, the current research lends credence to the significance of non-pharmacological therapy in managing fibromyalgia. Participants demonstrated positive advancements in subjective and objective pain assessments, as well as improvements in functional and emotional well-being. Full article

Forests appeared during the Middle to Late Devonian, but Devonian forests and their compositions are still rarely known. Xinhang forest was reported as the largest Devonian forest, with lycopsid trees of Guangdedendron micrum Wang et al. A fern-like plant Xinhangia spina Yang and Wang with shoots and anatomy, was previously described from this forest, but its habit and ecology remain unclear. From Xinhang forest, we now report more specimens of fern-like plants including X. spina and some unnamed plants in several beds. Prominent adventitious roots, spines and secondary xylem indicate that the stems of X. spina are largely procumbent to function as anchorage, absorption and support. Other fern-like plants with distinct roots or multiple slender branches also suggest procumbent habits. Xinhang forest is thus reconsidered as multispecific with a canopy of lycopsid trees and understory of diverse fern-like plants, which are adapted to the disturbed coastal environment. The composition of Xinhang forest may indicate a structural transition of the early forests’ dominator from fern-like plants to lycopsids. Full article

Our study aims to evaluate the effect of everolimus treatment on lung function in lung transplant (LT) patients with established chronic lung allograft dysfunction (CLAD). Methods: This retrospective study included LT patients in two reference LT units who started everolimus therapy to treat CLAD from October 2008 to October 2016. We assessed the variation in the maximum forced expiratory volume in the first second (FEV₁) before and after the treatment. Results: Fifty-seven patients were included in this study. The variation in the FEV₁ was −102.7 (149.6) mL/month before starting everolimus compared to −44.7 (109.6) mL/month within the first three months, +1.4 (63.5) mL/month until the sixth month, and −7.4 (46.2) mL/month until the twelfth month (p < 0.05). Glomerular filtrate remained unchanged after everolimus treatment [59.1 (17.5) mL/min per 1.73 m² at baseline and 60.9 (19.6) mL/min per 1.73 m², 57.7 (20.5) mL/min per 1.73 m², and 57.3 (17.8) mL/min per 1.73 m², at 1, 3, and 6 months, respectively] (p > 0.05). Everolimus was withdrawn in 22 (38.6%) patients. The median time to withdrawal was 14.1 (5.5–25.1) months. Conclusions: This study showed an improvement in FEV₁ decline in patients with CLAD treated with everolimus. However, the drug was withdrawn in a high proportion of patients. Full article

Fixed nitrogen species generated by the early Earth’s atmosphere are thought to be critical to the emergence of life and the sustenance of early metabolisms. A previous study estimated nitrogen fixation in the Hadean Earth’s N₂/CO₂-dominated atmosphere; however, that previous study only considered a limited chemical network that produces NO_x species (i.e., no HCN formation) via the thermochemical dissociation of N₂ and CO₂ in lightning flashes, followed by photochemistry. Here, we present an updated model of nitrogen fixation on Hadean Earth. We use the Chemical Equilibrium with Applications (CEA) thermochemical model to estimate lightning-induced NO and HCN formation and an updated version of KINETICS, the 1-D Caltech/JPL photochemical model, to assess the photochemical production of fixed nitrogen species that rain out into the Earth’s early ocean. Our updated photochemical model contains hydrocarbon and nitrile chemistry, and we use a Geant4 simulation platform to consider nitrogen fixation stimulated by solar energetic particle deposition throughout the atmosphere. We study the impact of a novel reaction pathway for generating HCN via HCN₂, inspired by the experimental results which suggest that reactions with CH radicals (from CH₄ photolysis) may facilitate the incorporation of N into the molecular structure of aerosols. When the HCN₂ reactions are added, we find that the HCN rainout rate rises by a factor of five in our 1-bar case and is about the same in our 2- and 12-bar cases. Finally, we estimate the equilibrium concentration of fixed nitrogen species under a kinetic steady state in the Hadean ocean, considering loss by hydrothermal vent circulation, photoreduction, and hydrolysis. These results inform our understanding of environments that may have been relevant to the formation of life on Earth, as well as processes that could lead to the emergence of life elsewhere in the universe. Full article

The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate scenarios, the potential distribution area of T. aureus was defined, providing a theoretical basis for conservation and management. Based on species distribution records, we utilized the Biomod2 platform to combine climate data from the BCC-CSM2-MR climate model, future shared socio-economic pathways, and altitude data. The potential distribution areas of T. aureus in the current (1970–2000s) and future SSP1_2.6 and SSP5_8.5 climate scenarios in China in 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) were predicted. The AUC and TSS values of the combined model based on five algorithms were greater than those of the single models, and the AUC value of the receiver operating characteristic curve was 0.990, indicating that the model had high reliability and accuracy. The screening of environmental variables showed that the habitat area of T. aureus in China was mainly affected by annual precipitation, precipitation in the driest month, the lowest temperature in the coldest month, temperature seasonality, elevation, and other factors. Under the current circumstances, the habitat area of T. aureus was mainly located in southern China, including Fujian, Guangdong, Guangxi, Hainan, Zhejiang, Yunnan, Guizhou, Hunan, Taiwan, and other provinces. The suitable area is approximately 138.95 × 10⁴ km²; among them, the highly suitable area of 34.43 × 10⁴ km² is a priority area in urgent need of protection. Under both SSP1_2.6 and SSP5_8.5, the population centroid tended to shift southward in the 2050s and 2070s, and began to migrate northeast in the 2090s. Temperature, rainfall, and altitude influenced the distribution of T. aureus. In the two climate scenarios, the habitat area of T. aureus declined to different degrees, and the reduction was most obvious in the SSP5_8.5 scenario; climate was the most likely environmental variable to cause a change in the geographical distribution. Climate change will significantly affect the evolution and potential distribution of T. aureus in China and will increase the risk of extinction. Accordingly, it is necessary to strengthen protection and to implement active and effective measures to reduce the negative impact of climate change on T. aureus. Full article

The expansion of forested areas through afforestation and reforestation is widely recognized as a highly effective natural solution for mitigating climate change. Accurately assessing the potential carbon uptake capacity of newly afforested areas requires modelling tools to estimate biomass stocks, including site index curves and biomass models. Given the unique conditions in terms of tree size, uniform spacing, and tree allometries observed in young afforestations compared to natural stands, specific tools are necessary. In Spain, over 800,000 ha has been afforested with native forest species since 1992, but specific modelling tools for these plantations are lacking. Using data from 370 stem analyses collected across an extensive network of plots in young afforestations, we developed dynamic height growth and site index models for the main native species (five pines and five oaks) commonly used in afforestation in Spain. We compared various nonlinear models, such as ADA (algebraic difference approach) and GADA (generalized algebraic difference approach) expansions. The developed site index models were then used to predict the total biomass stored in the afforestation. Our results underscore the necessity for specific site index models tailored to afforestations, as well as the potential of the established site index in predicting biomass and carbon fixation capacity in these young forests. Full article

The El Cielo Biosphere Reserve (CBR) stands as a vital forested region in eastern Mexico due to its high biodiversity in flora and fauna and provision of environmental services. This study established a network of 10 ring-width chronologies of different species within the CBR and adjacent watersheds. The objective was to analyze their climatic response and reconstruct the seasonal streamflow contribution of each sub-basin to the main stream, utilizing data from a gauge network of eight hydrological stations located at strategic locations of the CBR. With chronologies ranging from 116 to 564 years, most exhibited association with the accumulated streamflow between January and June. Based on the adjusted R², Akaike Information Criteria, and Variance Inflation Factor, the stepwise regression procedure was selected among different statistical methods for developing the reconstruction model. In spite of differences in the seasonal reconstructed periods, all the species showed potential to develop hydrological reconstructions as indicated by their common response to streamflow variability, as occurred in the wet years of 1976, 1993, 2000, and 2008, and dry years of 1980, 1982, 1996, and 2011. It was found that the response of the chronologies to gauge records increased as a function of the chronologies’ interseries correlation, average mean sensitivity, and distance of the tree-ring series to the gauge station. Streamflow reconstructions at the sub-basin level allowed a better understanding of the hydroclimatic variability characterizing the CBR, but also suggested the need to increase the network of chronologies for some particular sub-basins lacking tree-ring series to improve the reconstructed models. Full article

The utilization of radiative transfer models for interpreting remotely sensed data to evaluate forest disturbances is a cost-effective approach. However, the current radiative transfer modeling approaches are either too abstract (e.g., 1D models) or too complex (detailed 3D models). This study introduces a novel multilayer heterogeneous 3D radiative transfer framework with medium complexity, termed MART3D, for characterizing forest disturbances. MART3D generates 3D canopy structures accounting for the within-crown clumping by clustering leaves, which is modeled as a turbid medium, around branches, applicable for forests of medium complexity, such as temperate forests. It then automatically generates a multilayer forest with grass, shrub and several layers of trees using statistical parameters, such as the leaf area index and fraction of canopy cover. By employing the ray-tracing module within the well-established LargE-Scale remote sensing data and image Simulation model (LESS) as the computation backend, MART3D achieves a high accuracy (RMSE = 0.0022 and 0.018 for red and Near-Infrared bands) in terms of the bidirectional reflectance factor (BRF) over two RAMI forest scenes, even though the individual structures of MART3D are generated solely from statistical parameters. Furthermore, we demonstrated the versatility and user-friendliness of MART3D by evaluating the band selection strategy for computing the normalized burn ratio (NBR) to assess the composite burn index over a forest fire scene. The proposed MART3D is a flexible and easy-to-use tool for studying the remote sensing response under varying vegetation conditions. Full article

Light plays a crucial role in the growth of fruit trees, influencing not only nutrient absorption but also fruit appearance. Therefore, understanding fruit tree canopy light transmittance is essential for agricultural and forestry practices. However, traditional measurement methods, such as using a canopy analyzer, are time-consuming, labor-intensive, and susceptible to external influences, lacking convenience and automation. To address this issue, we propose a novel method based on point clouds to estimate light transmittance, with the Leaf Area Index (LAI) serving as the central link. Focusing on apple trees, we utilized handheld LiDAR for three-dimensional scanning of the canopy, acquiring point cloud data. Determining the optimal voxel size at 0.015 m via standardized point cloud mean spacing, we applied the Voxel-based Canopy Profile method (VCP) to estimate LAI. Subsequently, we established a function model between LAI and canopy light transmittance using a deep neural network (DNN), achieving an overall correlation coefficient R² of 0.94. This model was then employed to estimate canopy light transmittance in dwarfed and densely planted apple trees. This approach not only provides an evaluation standard for pruning effects in apple trees but also represents a critical step towards visualizing and intelligentizing light transmittance. Full article

Psoriatic disease (PsD) affects multiple clinical domains and causes a significant inflammatory burden in patients, requiring comprehensive evaluation and treatment. In recent years, new molecules such as JAK inhibitors (JAKinhibs) have been developed. These have very clear advantages: they act quickly, have a beneficial effect on pain, are well tolerated and the administration route is oral. Despite all this, there is still little scientific evidence in daily clinical practice. This observational, retrospective, single-center study was carried out in patients diagnosed with PsA in the last two years, who started treatment with Tofacitinib or Upadacitinib due to failure of a DMARD. The data of 32 patients were analyzed, and the majority of them (75%) started treatment with Tofacitinib. Most had moderate arthritis activity and mild psoriasis involvement according to activity indices. Both Tofacitinib and Upadacitinib demonstrated significant efficacy, with rapid and statistically significant improvement in joint and skin activity indices, C-reactive protein reduction, and objective measures of disease activity such as the number of painful and inflamed joints. Although there was some difference in the baseline characteristics of the cohort, treatment responses were comparable or even superior to those in the pivotal clinical trials. In addition, there was a low frequency of mild adverse events leading to treatment discontinuation and no serious adverse events. These findings emphasize the strong efficacy and tolerability of JAKinhibs in daily clinical practice, supporting their role as effective therapeutic options for patients with PsD. Full article

Introduction: This prospective study aims to illustrate the histopathological arterial changes in the popliteal artery in peripheral arterial disease of the lower limbs. Material and method: A total of 60 popliteal artery segments taken from patients who had undergone lower limb amputation were examined between April and June 2023. The degree of arterial stenosis, medial calcinosis, and the vasa vasorum changes in the arterial adventitia were quantified. The presence of risk factors for atherosclerosis was also observed. Results: Atherosclerotic plaque was found in all of the examined segments. Medial calcinosis was observed in 40 (66.6%) of the arterial segments. A positive association between the degree of arterial stenosis and the vasa vasorum changes in the arterial adventitia was also found (p = 0.025). The level of blood sugar and cholesterol were predictive factors for the severity of atherosclerosis. Conclusions: Atherosclerosis and medial calcinosis are significant in patients who underwent lower limb amputation. Medial calcinosis causes damage to the arterial wall and leads to a reduction in responsiveness to dilator stimuli. Full article

Many clinical studies have shown wide performance variation in tests to identify coronary artery disease (CAD). Coronary computed tomography angiography (CCTA) has been identified as an effective rule-out test but is not widely available in the USA, particularly so in rural areas. Patients in rural areas are underserved in the healthcare system as compared to urban areas, rendering it a priority population to target with highly accessible diagnostics. We previously developed a machine-learned algorithm to identify the presence of CAD (defined by functional significance) in patients with symptoms without the use of radiation or stress. The algorithm requires 215 s temporally synchronized photoplethysmographic and orthogonal voltage gradient signals acquired at rest. The purpose of the present work is to validate the performance of the algorithm in a frozen state (i.e., no retraining) in a large, blinded dataset from the IDENTIFY trial. IDENTIFY is a multicenter, selectively blinded, non-randomized, prospective, repository study to acquire signals with paired metadata from subjects with symptoms indicative of CAD within seven days prior to either left heart catheterization or CCTA. The algorithm’s sensitivity and specificity were validated using a set of unseen patient signals (n = 1816). Pre-specified endpoints were chosen to demonstrate a rule-out performance comparable to CCTA. The ROC-AUC in the validation set was 0.80 (95% CI: 0.78–0.82). This performance was maintained in both male and female subgroups. At the pre-specified cut point, the sensitivity was 0.85 (95% CI: 0.82–0.88), and the specificity was 0.58 (95% CI: 0.54–0.62), passing the pre-specified endpoints. Assuming a 4% disease prevalence, the NPV was 0.99. Algorithm performance is comparable to tertiary center testing using CCTA. Selection of a suitable cut-point results in the same sensitivity and specificity performance in females as in males. Therefore, a medical device embedding this algorithm may address an unmet need for a non-invasive, front-line point-of-care test for CAD (without any radiation or stress), thus offering significant benefits to the patient, physician, and healthcare system. Full article

The COVID-19 pandemic presents unique requirements for accessible, reliable testing, and many testing platforms and sampling techniques have been developed over the course of the pandemic. Not all test methods have been systematically compared to each other or a common gold standard, and the performance of tests developed in the early epidemic have not been consistently re-evaluated in the context of new variants. We conducted a repeated measures study with adult healthcare workers presenting for SARS-CoV-2 testing. Participants were tested using seven testing modalities. Test sensitivity was compared using any positive PCR test as the gold standard. A total of 325 individuals participated in the study. PCR tests were the most sensitive (saliva PCR 0.957 ± 0.048, nasopharyngeal PCR 0.877 ± 0.075, oropharyngeal PCR 0.849 ± 0.082). Standard nasal rapid antigen tests were less sensitive but roughly equivalent (BinaxNOW 0.613 ± 0.110, iHealth 0.627 ± 0.109). Oropharyngeal rapid antigen tests were the least sensitive (BinaxNOW 0.400 ± 0.111, iHealth brands 0.311 ± 0.105). PCR remains the most sensitive testing modality for the diagnosis of COVID-19 and saliva PCR is significantly more sensitive than oropharyngeal PCR and equivalent to nasopharyngeal PCR. Nasal AgRDTs are less sensitive than PCR but have benefits in convenience and accessibility. Saliva-based PCR testing is a viable alternative to traditional swab-based PCR testing for the diagnosis of COVID-19. Full article

(1) Background: Computed tomography (CT) plays a paramount role in the characterization and follow-up of COVID-19. Several score systems have been implemented to properly assess the lung parenchyma involved in patients suffering from SARS-CoV-2 infection, such as the visual quantitative assessment score (VQAS) and software-based quantitative assessment score (SBQAS) to help in managing patients with SARS-CoV-2 infection. This study aims to investigate and compare the diagnostic accuracy of the VQAS and SBQAS with two different types of software based on artificial intelligence (AI) in patients affected by SARS-CoV-2. (2) Methods: This is a retrospective study; a total of 90 patients were enrolled with the following criteria: patients’ age more than 18 years old, positive test for COVID-19 and unenhanced chest CT scan obtained between March and June 2021. The VQAS was independently assessed, and the SBQAS was performed with two different artificial intelligence-driven software programs (Icolung and CT-COPD). The Intraclass Correlation Coefficient (ICC) statistical index and Bland–Altman Plot were employed. (3) Results: The agreement scores between radiologists (R1 and R2) for the VQAS of the lung parenchyma involved in the CT images were good (ICC = 0.871). The agreement score between the two software types for the SBQAS was moderate (ICC = 0.584). The accordance between Icolung and the median of the visual evaluations (Median R1–R2) was good (ICC = 0.885). The correspondence between CT-COPD and the median of the VQAS (Median R1–R2) was moderate (ICC = 0.622). (4) Conclusions: This study showed moderate and good agreement upon the VQAS and the SBQAS; enhancing this approach as a valuable tool to manage COVID-19 patients and the combination of AI tools with physician expertise can lead to the most accurate diagnosis and treatment plans for patients. Full article

Breast cancer is the most prevalent type of cancer in women. Risk factor assessment can aid in directing counseling regarding risk reduction and breast cancer surveillance. This research aims to (1) investigate the relationship between various risk factors and breast cancer incidence using the BCSC (Breast Cancer Surveillance Consortium) Risk Factor Dataset and create a prediction model for assessing the risk of developing breast cancer; (2) diagnose breast cancer using the Breast Cancer Wisconsin diagnostic dataset; and (3) analyze breast cancer survivability using the SEER (Surveillance, Epidemiology, and End Results) Breast Cancer Dataset. Applying resampling techniques on the training dataset before using various machine learning techniques can affect the performance of the classifiers. The three breast cancer datasets were examined using a variety of pre-processing approaches and classification models to assess their performance in terms of accuracy, precision, F-1 scores, etc. The PCA (principal component analysis) and resampling strategies produced remarkable results. For the BCSC Dataset, the Random Forest algorithm exhibited the best performance out of the applied classifiers, with an accuracy of 87.53%. Out of the different resampling techniques applied to the training dataset for training the Random Forest classifier, the Tomek Link exhibited the best test accuracy, at 87.47%. We compared all the models used with previously used techniques. After applying the resampling techniques, the accuracy scores of the test data decreased even if the training data accuracy increased. For the Breast Cancer Wisconsin diagnostic dataset, the K-Nearest Neighbor algorithm had the best accuracy with the original dataset test set, at 94.71%, and the PCA dataset test set exhibited 95.29% accuracy for detecting breast cancer. Using the SEER Dataset, this study also explores survival analysis, employing supervised and unsupervised learning approaches to offer insights into the variables affecting breast cancer survivability. This study emphasizes the significance of individualized approaches in the management and treatment of breast cancer by incorporating phenotypic variations and recognizing the heterogeneity of the disease. Through data-driven insights and advanced machine learning, this study contributes significantly to the ongoing efforts in breast cancer research, diagnostics, and personalized medicine. Full article

Cataracts, known for lens clouding and being a common cause of visual impairment, persist as a primary contributor to vision loss and blindness, presenting notable diagnostic and prognostic challenges. This work presents a novel framework called the Cataract States Detection Network (CSDNet), which utilizes deep learning methods to improve the detection of cataract states. The aim is to create a framework that is more lightweight and adaptable for use in environments or devices with limited memory or storage capacity. This involves reducing the number of trainable parameters while still allowing for effective learning of representations from data. Additionally, the framework is designed to be suitable for real-time or near-real-time applications where rapid inference is essential. This study utilizes cataract and normal images from the Ocular Disease Intelligent Recognition (ODIR) database. The suggested model employs smaller kernels, fewer training parameters, and layers to efficiently decrease the number of trainable parameters, thereby lowering computational costs and average running time compared to other pre-trained models such as VGG19, ResNet50, DenseNet201, MIRNet, Inception V3, Xception, and Efficient net B0. The experimental results illustrate that the proposed approach achieves a binary classification accuracy of 97.24% (normal or cataract) and an average cataract state detection accuracy of 98.17% (normal, grade 1—minimal cloudiness, grade 2—immature cataract, grade 3—mature cataract, and grade 4—hyper mature cataract), competing with state-of-the-art cataract detection methods. The resulting model is lightweight at 17 MB and has fewer trainable parameters (175, 617), making it suitable for deployment in environments or devices with constrained memory or storage capacity. With a runtime of 212 ms, it is well-suited for real-time or near-real-time applications requiring rapid inference. Full article

We present a case of a 59-year-old male diagnosed with polycythemia vera (PV) for many years, who presented with a relatively abrupt onset of heavy constitutional symptoms, including fatigue, night sweats, and a 10% weight loss over 6 weeks. Despite the known initial diagnosis of PV, the presence of profound B-symptoms prompted further investigation. A positron emission tomography/computed tomography (PET/CT) scan with ¹⁸F-Fluorodeoxyglucose ([¹⁸F]FDG) was performed to exclude malignant diseases. The [¹⁸F]FDG PET/CT revealed intense metabolic activity in the bone marrow of the proximal extremities and trunk skeleton, as well as a massively enlarged spleen with increased metabolic activity. Histopathologically, a transformation to myelofibrosis was revealed on a bone marrow biopsy. The case intends to serve as an exemplification for [¹⁸F]FDG PET/CT in PV with transformation to myelofibrosis (post-PV myelofibrosis). Full article

Prediction of short-term mortality in patients with acute decompensation of liver cirrhosis could be improved. We aimed to develop and validate two machine learning (ML) models for predicting 28-day and 90-day mortality in patients hospitalized with acute decompensated liver cirrhosis. We trained two artificial neural network (ANN)-based ML models using a training sample of 165 out of 290 (56.9%) patients, and then tested their predictive performance against Model of End-stage Liver Disease-Sodium (MELD-Na) and MELD 3.0 scores using a different validation sample of 125 out of 290 (43.1%) patients. The area under the ROC curve (AUC) for predicting 28-day mortality for the ML model was 0.811 (95%CI: 0.714- 0.907; p < 0.001), while the AUC for the MELD-Na score was 0.577 (95%CI: 0.435–0.720; p = 0.226) and for MELD 3.0 was 0.600 (95%CI: 0.462–0.739; p = 0.117). The area under the ROC curve (AUC) for predicting 90-day mortality for the ML model was 0.839 (95%CI: 0.776- 0.884; p < 0.001), while the AUC for the MELD-Na score was 0.682 (95%CI: 0.575–0.790; p = 0.002) and for MELD 3.0 was 0.703 (95%CI: 0.590–0.816; p < 0.001). Our study demonstrates that ML-based models for predicting short-term mortality in patients with acute decompensation of liver cirrhosis perform significantly better than MELD-Na and MELD 3.0 scores in a validation cohort. Full article