This study aimed to investigate the prevalence and determinants of glucose metabolism abnormalities and their impact on long-term clinical outcomes in kidney transplant recipients (KTxps). A retrospective analysis of 832 KTxps (2004–2020) was performed. Patients were assessed at 1 (T1), 6 (T6), and 12 (T12) months post-transplantation and clinically followed for an average of 103 ± 60 months. At T6, 484 patients underwent an oral glucose tolerance test for the diagnosis of alterations in glucose metabolism (AMG+) or post-transplant diabetes mellitus (PTDM+). The prevalence of pre-transplant diabetes was 6.2%, with 22.4% of PTDM+ within the 1st year. Patients with AMG were older and exhibited altered lipid profiles, higher body mass index, and increased inflammatory indices. Age at transplantation, lipid profile, and inflammatory status were significant determinants of PTDM. Graft loss was unaffected by glucose metabolism alterations. Survival analysis demonstrated significantly worse long-term survival for KTxps with diabetes (pre- and PTDM+, p = 0.04). In a comparison of the ND and PTDM+ groups, no significant differences in death with a functioning graft were found. The AMG+ group exhibited worse survival (p < 0.001) than AMG−, even after excluding patients with diabetes mellitus. Future randomized controlled trials are necessary to delve deeper into this subject, specifically examining the effects of new antidiabetic treatments. Full article

Accurate bird species recognition is crucial for ecological conservation, wildlife monitoring, and biological research, yet it poses significant challenges due to the high variability within species and the subtle similarities between different species. This paper introduces an automatic bird species recognition method from images that leverages feature enhancement and contrast learning to address these challenges. Our method incorporates a multi-scale feature fusion module to comprehensively capture information from bird images across diverse scales and perspectives. Additionally, an attention feature enhancement module is integrated to address noise and occlusion within images, thus enhancing the model’s robustness. Furthermore, employing a siamese network architecture allows effective learning of common features within instances of the same class and distinctions between different bird species. Evaluated on the CUB200-2011 dataset, our proposed method achieves state-of-the-art performance, surpassing existing methods with an accuracy of 91.3% and F1 score of 90.6%. Moreover, our approach showcases a notable advantage in scenarios with limited training data. When utilizing only 5% of the training data, our model still achieves a recognition accuracy of 65.2%, which is significantly higher than existing methods under similar data constraints. Notably, our model exhibits faster execution times compared to existing methods, rendering it suitable for real-time applications. Full article

Supply chain security is a major prerequisite for China’s successful industrial modernization, while the digital economy has significantly contributed to industrial transformation and upgrading. This study considers China’s wooden furniture industry as its research object, constructing an evaluation index system of the digital economy and supply chain security of the wooden furniture industry. Then, it studies the impact of the digital economy on supply chain security through theoretical analysis and empirical methods using the two-way fixed model of provinces and time. The findings demonstrate that the digital economy effectively enhances the level of supply chain security in China’s wooden furniture industry, further validating the digital economy’s positive externality impact on the traditional real economy. The impact mechanism test shows that inventory turnover capacity is the focal point for the digital economy to improve the supply chain security of the wood furniture industry, specifying the starting point for that industry’s digital transformation. The heterogeneity findings show that the role of the digital economy in improving the wood furniture industry’s level of supply chain security is more significant in inland areas than in coastal areas. Additional analyses found a threshold effect of the digital economy’s impact on supply chain security, indicating its limitations. This study explores the impact of the digital economy on the real economy from a traditional manufacturing industry, enriching research on the positive externalities of the digital economy as well as providing a reference for traditional manufacturing industries, such as that of wooden furniture, to probe the embedding points of the digital economy and appropriate digital transformation. Full article

This research paper introduces a new topology for multilevel inverters, emphasizing the reduction of harmonic distortion and the optimization of the component count. The complexity of an inverter is determined by the number of power switches, which is significantly reduced in the presented topology, as fewer switches require fewer driver circuits. In this proposed topology, a new single-phase generalized multilevel inverter is analyzed with an equal magnitude of voltage supply. A 9-level, 11-level, or 13-level symmetrical inverter with RL load is analyzed in MATLAB/Simulink 2019b and then experimentally validated using the dSPACE-1103 controller. The experimental verification of the load voltage and current with different modulation indices is also presented. The analysis of the proposed topology concludes that the total required number of components is lower than that necessary for the classical inverter topologies, as well as for some new proposed multilevel inverters that are also compared with the proposed topology in terms of gate driver circuits, power switches, and DC sources, which thereby enhances the goodness of the proposed topology. Thus, a comparison of this inverter with the other topologies validates its acceptance. Full article

Urban green space (UGS) has been recognized as a key factor in enhancing the urban ecosystem balance, particularly in arid areas. It is often considered an effective means to mitigate the urban heat island (UHI) effect. In this study, the reference comparison method was utilized to optimize the process of nighttime lighting data; the random forest classification method was employed to extract UGS data; and the radiative transfer method was applied in land surface temperature (LST) inversion. Additionally, moving window analysis was conducted to assess the robustness of the results. The objective of this research was to analyze the spatial distribution characteristics of UGS and LST and to explore their bivariate local spatial autocorrelations by calculating four landscape metrics, including the aggregation index (AI), edge density (ED), patch density (PD), and area-weighted mean shape index (Shape_am). It was found that the distribution of UGS in the study area was uneven, with higher temperatures in the eastern and western regions and lower temperatures in the central and southern regions. The results also revealed that ED, PD, and Shape_am were negatively correlated with LST, with correlation coefficients being −0.469, −0.388, and −0.411, respectively, indicating that UGS in these regions were more effective in terms of cooling effect. Conversely, AI was found to be positively correlated with LST (Moran’ I index of 0.449), indicating that surface temperatures were relatively higher in regions of high aggregation. In essence, the fragmented, complex, and evenly distributed green patches in the study area provided a better cooling effect. These findings should persuade decision makers and municipal planners to allocate more UGS in cities for UHI alleviation to improve quality of life and enhance recreational opportunities. Full article

Water pollution remains a critical concern, one necessitated by rapidly increasing industrialization and urbanization. Among the various strategies for water purification, membrane technology stands out, with polyethersulfone (PES) often being the material of choice due to its robust mechanical properties, thermal stability, and chemical resistance. However, PES-based membranes tend to exhibit low hydrophilicity, leading to reduced flux and poor anti-fouling performance. This study addresses these limitations by incorporating titanium dioxide nanotubes (TiO₂NTs) into PES nanofiltration membranes to enhance their hydrophilic properties. The TiO₂NTs, characterized through FTIR, XRD, BET, and SEM, were embedded in PES at varying concentrations using a non-solvent induced phase inversion (NIPS) method. The fabricated mixed matrix membranes (MMMs) were subjected to testing for water permeability and solute rejection capabilities. Remarkably, membranes with a 1 wt.% TiO₂NT loading displayed a significant increase in pure water flux, from 36 to 72 L m² h⁻¹ bar⁻¹, a 300-fold increase in selectivity compared to the pristine sample, and a dye rejection of 99%. Furthermore, long-term stability tests showed only a slight reduction in permeate flux over a time of 36 h, while dye removal efficiency was maintained, thus confirming the membrane’s stability. Anti-fouling tests revealed a 93% flux recovery ratio, indicating excellent resistance to fouling. These results suggest that the inclusion of TiO₂ NTs offers a promising avenue for the development of efficient and stable anti-fouling PES-based membranes for water purification. Full article

The multi-scale study of rock properties is a necessary step in the planning of oil and gas reservoir developments. The amount of core samples available for research is usually limited, and some of the samples can be distracted. The investigation of core reconstruction possibilities is an important task. An approach to the real-size reconstruction of porous media with a given (target) porosity and permeability by controlling the parameters of FFF 3D printing using CT images of the original core is proposed. Real-size synthetic core specimens based on CT images were manufactured using FFF 3D printing. The possibility of reconstructing the reservoir properties of a sandstone core sample was proven. The results of gas porometry measurements showed that the porosity of specimens No.32 and No.46 was 13.5% and 12.8%, and the permeability was 442.3 mD and 337.8 mD, respectively. The porosity of the original core was 14% and permeability was 271 mD. It was found that changing the layer height and nozzle diameter, as well as the retract and restart distances, has a direct effect on the porosity and permeability of synthetic specimens. This study shows that porosity and permeability of synthetic specimens depend on the flow of the material and the percentage of overlap between the infill and the outer wall. Full article

Influenza A (H1N1) viruses are prone to antigenic mutations and are more variable than other influenza viruses. Therefore, they have caused continuous harm to human public health since the pandemic in 2009 and in recent times. Influenza A (H1N1) can be prevented and treated in various ways, such as direct inhibition of the virus and regulation of human immunity. Among antiviral drugs, the use of natural products in treating influenza has a long history, and natural medicine has been widely considered the focus of development programs for new, safe anti-influenza drugs. In this paper, we focus on influenza A (H1N1) and summarize the natural product-derived phytochemicals for influenza A virus (H1N1) prevention and treatment, including marine natural products, flavonoids, alkaloids, terpenoids and their derivatives, phenols and their derivatives, polysaccharides, and derivatives of natural products for prevention and treatment of influenza A (H1N1) virus. We further discuss the toxicity and antiviral mechanism against influenza A (H1N1) as well as the druggability of natural products. We hope that this review will facilitate the study of the role of natural products against influenza A (H1N1) activity and provide a promising alternative for further anti-influenza A drug development. Full article

Battery cell finalization is a crucial process chain in battery manufacturing, contributing to a significant share of CAPEX and OPEX. Thus, there is a high cost-saving potential by improving the process chain. This research paper investigates various crucial facets of the cell finalization process in battery cell production through an expert survey. These include investment cost allocation, potential cost savings in sub-processes, reject generation, early detection of faulty cells, quality measurement techniques, and the utilization of inline data for early quality determination and real-time process control during the formation process. A solution approach for the implementation of electrochemical impedance spectroscopy for inline early quality determination is given. The results yield valuable insights for optimizing the formation process and enhancing product quality. Full article

Traditionally, the assessment of heavy metal concentrations using remote sensing technology is sample-intensive, with expensive model development. Using a mining area case study of Daxigou, China, we propose a cross-time-domain transfer learning model to monitor heavy metal pollution using samples collected from different time domains. Specifically, spectral indices derived from Landsat 8 multispectral images, terrain, and other auxiliary data correlative to soil heavy metals were prepared. A cross time-domain sample transfer learning model proposed in the paper based on the TrAdaBoost algorithm was used for the Cu content mapping in the topsoil by selective use of soil samples acquired in 2017 and 2019. We found that the proposed model accurately estimated the concentration of Cu in the topsoil of the mining area in 2019 and performed better than the traditional TrAdaBoost algorithms. The goodness of fit (R²) of the test set increased from 0.55 to 0.66; the relative prediction deviation (RPD) increased from 1.37 to 1.76; and finally, the root-mean-square deviation (RMSE), decreased from 8.33 to 7.24 mg·kg⁻¹.The proposed model is potentially applicable to more accurate and inexpensive monitoring of heavy metals, facilitating remediation-related efforts. Full article

Nanomagnetism and spintronics are currently active areas of research, with one of the main goals being the creation of low-energy-consuming magnetic memories based on nanomagnet switching. These types of devices could also be implemented in neuromorphic computing by crafting artificial neurons (ANs) that emulate the characteristics of biological neurons through the implementation of neuron models such as the widely used leaky integrate-and-fire (LIF) with a refractory period. In this study, we have carried out numerical simulations of a 120 nm diameter, 250 nm length ferromagnetic nanowire (NW) with the aim of exploring the design of an artificial neuron based on the creation and destruction of a Bloch-point domain wall. To replicate signal integration, we applied pulsed trains of spin currents to the opposite faces of the ferromagnetic NW. These pulsed currents (previously studied only in the continuous form) are responsible for inducing transitions between the stable single vortex (SV) state and the metastable Bloch point domain wall (BP-DW) state. To ensure the system exhibits leak and refractory properties, the NW was placed in a homogeneous magnetic field of the order of mT in the axial direction. The suggested configuration fulfills the requirements and characteristics of a biological neuron, potentially leading to the future creation of artificial neural networks (ANNs) based on reversible changes in the topology of magnetic NWs. Full article

Bacterial infections are the second-leading cause of death, globally. The prevalence of antibacterial resistance has kept the demand strong for the development of new and potent drug candidates. It has been demonstrated that Src protein tyrosine kinases (TKs) play an important role in the regulation of inflammatory responses to tissue injury, which can trigger the onset of several severe diseases. We carried out a search for novel Src protein TK inhibitors, commencing from reported highly potent anti-bacterial compounds obtained using the Mannich reaction, using a combination of e-pharmacophore modeling, virtual screening, ensemble docking, and core hopping. The top-scoring compounds from ligand-based virtual screening were modified using protein structure-based design approaches, and their binding to the Src homology-2 domain of p56^lck TK was predicted using ensemble molecular docking. We have prepared a database of 202 small molecules and have identified six novel top hits that can be subjected to further investigation. We have also performed in silico ADMET property prediction for the hit compounds. This combined computer-aided drug design approach can serve as a starting point for identifying novel TK inhibitors that could be further subjected to in vitro studies and validation of antimicrobial activity. Full article

Virtual Reality Exposure Therapy (VRET), particularly immersive Virtual Reality Exposure Therapy (iVRET), has gained attraction as an innovative approach in exposure therapy (ET), notably for some anxiety disorders with a fear of contamination component, such as spider phobia (SP) and obsessive–compulsive disorder (OCD). This systematic work investigates iVRET’s effectiveness in modulating disgust emotion—a shared aberrant feature across these disorders. Recent reviews have evaluated VRET’s efficacy against in vivo ET. However, emerging evidence also highlights iVRET’s potential in diminishing atypical disgust and related avoidance behaviors, expanding beyond traditional fear-focused outcomes. Our systematic synthesis, adhering to PRISMA guidelines, aims to fill this gap by assessing iVRET’s efficacy in regulating disgust emotion within both clinical and at-risk populations, identified through standardized questionnaires and subjective disgust ratings. This research analyzes data from eight studies on clinical populations and five on healthy populations, offering an insight into iVRET’s potential to mitigate the aberrant disgust response, a common transdiagnostic feature in varied psychopathologies. The findings support iVRET’s clinical relevance in disgust management, providing evidence for a broader therapeutic application of iVRET and pointing out the need for more focused and complete investigations in this emergent field. Full article

This study evaluates the efficacy of the salts sodium metabisulfite (SMB), ammonium bicarbonate, sodium bicarbonate, and potassium dihydrogen orthophosphate first in vitro against the main postharvest fruit rot fungi, Alternaria alternata, Botrytis cinerea, Penicillium italicum, and Penicillium digitatum. Results showed that 0.2% SMB completely inhibited the mycelium growth of the fungal species. Ammonium bicarbonate and sodium bicarbonate were less effective at 0.2% in inhibiting mycelial growth, ranging from 57.6% to 77.6%. The least effective was potassium dihydrogen orthophosphate. Experiments were also performed in vivo on wounded apples inoculated with the most pathogenic fungus, B. cinerea, and treated with SMB at concentrations of 0.2, 0.5, 1, 2, and 3%, both preventively and curatively. Results based on the decay size showed that SMB, when used as a preventive treatment, had a reduced efficacy, even with the highest concentration. However, this salt proved to be very effective at 0.5% in curative treatment since the decay was completely blocked. Our results suggest that the appropriate concentration of SMB for post-harvest treatment is 0.5% as a curative treatment. On the other hand, the 1% dose induced the onset of phytotoxicity around the wound. To assess the extent of the phytotoxicity reaction, higher concentrations of 1–4% SMB were applied to wounded fruit. Apples and oranges were inoculated or not with B. cinerea and P. digitatum, respectively. Doses of 1–4% induced phytotoxicity in the form of a discolored ring surrounding the wound on the epidermis of the fruit; this phytotoxicity enlarged as the concentration of SMB increased. The phytotoxic features were similar on apples and oranges. The methodological procedure made it possible to carry out a quantitative assessment of SMB phytotoxicity. This method is proposed as an easy-to-use technique for quantitatively estimating the phytotoxicity of antifungal compounds on post-harvest fruit. Full article

Circulating cfRNA in plasma has emerged as a fascinating area of research with potential applications in disease diagnosis, monitoring, and personalized medicine. Circulating RNA sequencing technology allows for the non-invasive collection of important information about the expression of target genes, eliminating the need for biopsies. This comprehensive review aims to provide a detailed overview of the current knowledge and advancements in the study of plasma cfRNA, focusing on its diverse landscape and biological functions, detection methods, its diagnostic and prognostic potential in various diseases, challenges, and future perspectives. Full article

The safe and efficient production of marine natural gas hydrates faces the challenges of seabed geological risk issues. Geological risk issues can be categorized from weak to strong threats in four aspects: sand production, wellbore instability, seafloor subsidence, and submarine landslides, with the potential risk of natural gas leakage, and the geological risk problems that can cause secondary disasters dominated by gas eruptions and seawater intrusion. If the gas in a reservoir is not discharged in a smooth and timely manner during production, it can build up inside the formation to form super pore pressure leading to a sudden gas eruption when the overburden is damaged. There is a high risk of overburden destabilization around production wells, and reservoirs are prone to forming a connection with the seafloor resulting in seawater intrusion under osmotic pressure. This paper summarizes the application of field observation, experimental research, and numerical simulation methods in evaluating the stability problem of the seafloor surface. The theoretical model of multi-field coupling can be used to describe and evaluate the seafloor geologic risk issues during depressurization production, and the controlling equations accurately describing the characteristics of the reservoir are the key theoretical basis for evaluating the stability of the seafloor geomechanics. It is necessary to seek a balance between submarine formation stability and reservoir production efficiency in order to assess the optimal production and predict the region of plastic damage in the reservoir. Prediction and assessment allow measures to be taken at fixed points to improve reservoir mechanical stability with the numerical simulation method. Hydrate reservoirs need to be filled with gravel to enhance mechanical strength and permeability, and overburden need to be grouted to reinforce stability. Full article

Transmission electron microscopy imaging provides a unique opportunity to inspect the detailed structure of infected lung cells with SARS-CoV-2. Unlike previous studies, this novel study aims to investigate COVID-19 classification at the lung cellular level in response to SARS-CoV-2. Particularly, differentiating between healthy and infected human alveolar type II (hAT2) cells with SARS-CoV-2. Hence, we explore the feasibility of deep transfer learning (DTL) and introduce a highly accurate approach that works as follows: First, we downloaded and processed 286 images pertaining to healthy and infected hAT2 cells obtained from the electron microscopy public image archive. Second, we provided processed images to two DTL computations to induce ten DTL models. The first DTL computation employs five pre-trained models (including DenseNet201 and ResNet152V2) trained on more than one million images from the ImageNet database to extract features from hAT2 images. Then, it flattens and provides the output feature vectors to a trained, densely connected classifier with the Adam optimizer. The second DTL computation works in a similar manner, with a minor difference in that we freeze the first layers for feature extraction in pre-trained models while unfreezing and jointly training the next layers. The results using five-fold cross-validation demonstrated that TFeDenseNet201 is 12.37× faster and superior, yielding the highest average ACC of 0.993 (F1 of 0.992 and MCC of 0.986) with statistical significance ( from a t-test) compared to an average ACC of 0.937 (F1 of 0.938 and MCC of 0.877) for the counterpart (TFtDenseNet201), showing no significance results ( from a t-test). Full article

This paper proposes transforming actual monitoring data into risk quantities and establishing a Long Short-Term Memory (LSTM) safety risk warning model for predicting the deformation of super-large and ultra-deep foundation pits in river–round gravel strata based on safety evaluation methods. Using this model, short-term deformation predictions at various monitoring points of the foundation pits are made and compared with monitoring data. The results from the LSTM safety risk warning model indicate an absolute error range between the predicted deformation values and on-site monitoring values of −0.24 to 0.16 mm, demonstrating the model’s accuracy in predicting pit deformation. Additionally, calculations reveal that both the overall risk level based on on-site monitoring data and the overall safety risk level based on predicted data are classified as level four. The acceptance criteria for the overall risk level of the foundation pit are defined as “unacceptable and requiring decision-making”, with the risk warning control scheme being “requiring decision-making, formulation of control, and warning measures”. These research findings offer valuable insights for predicting and warning about safety risks in similar foundation pit engineering projects. Full article

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD. Full article

EpCAM is expressed in many epithelial tumors and is used for the distinction of malignant mesotheliomas from adenocarcinomas and as a surrogate pan-epithelial marker. A tissue microarray containing 14,832 samples from 120 different tumor categories was analyzed by immunohistochemistry. EpCAM staining was compared with TROP2 and CKpan. EpCAM staining was detectable in 99 tumor categories. Among 78 epithelial tumor types, the EpCAM positivity rate was ≥90% in 60 categories—including adenocarcinomas, neuroendocrine neoplasms, and germ cell tumors. EpCAM staining was the lowest in hepatocellular carcinomas, adrenocortical tumors, renal cell neoplasms, and in poorly differentiated carcinomas. A comparison of EpCAM and CKpan staining identified a high concordance but EpCAM was higher in testicular seminomas and neuroendocrine neoplasms and CKpan in hepatocellular carcinomas, mesotheliomas, and poorly differentiated non-neuroendocrine tumors. A comparison of EpCAM and TROP2 revealed a higher rate of TROP2 positivity in squamous cell carcinomas and lower rates in many gastrointestinal adenocarcinomas, testicular germ cell tumors, neuroendocrine neoplasms, and renal cell tumors. These data confirm EpCAM as a surrogate epithelial marker for adenocarcinomas and its diagnostic utility for the distinction of malignant mesotheliomas. In comparison to CKpan and TROP2 antibodies, EpCAM staining is particularly common in seminomas and in neuroendocrine neoplasms. Full article

The degradation and loss of natural wetlands has caused severe crises for wetland taxa. Meanwhile, constructed wetlands are expanding significantly and facing dramatic environmental changes. Exploring the responses of wetland organisms, particularly zooplankton, may have important implications for the management of wetlands. Environmental and zooplankton samples were collected from 34 subsidence wetlands created by underground coal mining across the North China Plain in August 2021. We used generalized linear models and redundancy analysis to test zooplankton responses to environmental variables, with the relative importance quantified by variation partitioning. We identified 91 species, divided into 7 functional groups, with the highest density of rotifer filter feeders (RF, 2243.4 ± 499.4 ind./L). Zooplankton species richness was negatively correlated with electrical conductivity (EC), chlorophyll-a, total phosphorus, and pH. The Shannon–Weiner and Pielou evenness indices were positively correlated with transparency and negatively correlated with the photovoltaic panel area (AS). Rotifer predators (RCs) and RF densities were positively correlated with cropland area and dissolved oxygen, but negatively correlated with AS. Small crustacean filter feeders positively correlated with AS, whereas medium crustacean feeders (MCFs) positively correlated with EC. AS was the most critical variable affecting the zooplankton community. Our study showed that the spatial pattern of zooplankton communities was shaped by environmental heterogeneity across the subsidence wetlands, providing implications for the management and conservation of these constructed wetlands. Full article

Alcoholism presents a significant health concern with notable socioeconomic implications. Alcohol withdrawal syndrome (AWS) can manifest when individuals cease or drastically reduce their alcohol consumption after prolonged use. Non-alcoholic fatty liver disease (NAFLD) is characterized by substantial lipid accumulation in the liver cells of individuals with no history of alcohol consumption. There is evidence suggesting an association between cognitive impairment and both conditions. This study aimed to evaluate cognitive impairment in patients with NAFLD and AWS using the Mini-Mental State Examination (MMSE). This study involved 120 patients admitted to two hospitals in Craiova, Romania. Results indicated that patients with NAFLD did not exhibit cognitive impairment as measured by MMSE (Mean = 29.27, SD = 0.785). Conversely, patients with AWS showed more pronounced cognitive dysfunction, with a mean MMSE score at admission of 16.60 ± 4.097 and 24.60 ± 2.832 after 2 weeks under treatment with Vitamins B1 and B6 and Cerebrolysin. Additionally, our findings suggested that cognitive dysfunction among alcohol consumers was correlated with the severity of clinical symptoms, as demonstrated by the severity of tremors in our study. The two-week period under treatment and alcohol withdrawal was insufficient for cognitive function to return to normal levels. Observational studies on longer periods of time are advised. Full article