Articles

Cytomegalovirus (CMV) colitis is classically seen in immunocompromised individuals, such as those with HIV, organ transplants, or receiving immunosuppressive therapy. It is exceedingly rare in immunocompetent patients and can pose a diagnostic challenge, especially when it presents as a mass-like lesion mimicking colon cancer. We report a case of a 78-year-old immunocompetent male who presented with hematochezia and was found to have a bleeding cecal mass initially suspicious for adenocarcinoma. Subsequent histopathology revealed CMV colitis. This case emphasizes the importance of considering CMV colitis in the differential diagnosis of colonic masses, even in immunocompetent hosts.

Objective: To provide a rigorous, multidisciplinary synthesis of the epidemiological, genetic, immunological, and environmental factors contributing to Premature Ovarian Failure (POF), with particular attention to regional disparities, occupational exposures, the impact of chemotherapy, the occurrence and recurrence of endometrosis, and emerging fertility preservation strategies. Design: A structured literature review with an emphasis on recent advances in genetic and immunological understanding.Setting: Academic research and clinical insights from multidisciplinary contributors.Patients: Individuals diagnosed with POF as reported in the literature.Interventions: Review of literature concerning epidemiology, genetic mutations, immunological disorders, and surgical outcomes linked to POF.Main outcome measures: Identification of both established and emerging risk factors, validation of genetic and immunological markers, and clarification of diagnostic and preventive clinical approaches.Results: The prevalence of POF varies globally, affecting 1% of women under 40. Genetic factors, particularly mutations in the FMR1 and BMP15 genes, play a significant role, alongside autoimmune diseases. Chemotherapy is a leading iatrogenic cause, while endometriosis and ovarian cyst surgeries significantly contribute to diminished ovarian reserve.Conclusion: POF is a multifactorial condition with rising incidence in specific subgroups. Improved early detection, standardized biomarker use, and expanded access to fertility preservation are essential. Targeted genomic and occupational risk screening may enable personalized interventions. Further genomic studies are needed to elucidate rare mutations and their impact.

Adherence, in a healthcare context, refers to the extent to which a person’s behavior aligns with recommendations from a healthcare provider. This can involve taking medication as prescribed, following a specific diet, or adhering to lifestyle changes. Although it is known that adherence is critical and requires a special approach to health, in Mexico, there are few studies on this issue. We confirm the information collected from 786 patients using an original questionnaire (AFEC) in Spanish, previously validated. Perfect adherence was detected in only 179 (22.8%), good adherence in 334 (42.5%), and nonadherence in 452 (57.5%). The age, obesity, and number of comorbidities were significantly associated with non-adherence (p < 0.05). Special strategies to approach this critical problem in health need to be reconsidered in Mexico. Non-adherence may help explain the low control rate of Mexican patients and the high rate of cardiovascular mortality.

Fish are important organisms as they indicate the ecological and environmental processes and the producer-consumer interactions. The fish samples of Cirrhinus mrigala and Oreochromis niloticus were collected from the Yamuna River at Allahabad (now Prayagraj), India, from August 2011 to July 2012. During the study period, 335 specimens of Cirrhinus mrigala and 516 specimens of Oreochromis niloticus were examined for the estimation of the age pyramid. The age groups varied from 0+ to 10+, and a heavy bottom or broad-based age pyramid was recorded in C. mrigala. The age groups varied from 0+ to 6+, and Urn shaped age pyramid was recorded in O. niloticus. The age group 2+ dominated by virtue of numbers in the lower stretch of the Yamuna River in both species of stock.

Background: Acute Respiratory Distress Syndrome (ARDS) is a clinically, radiologically, and biologically heterogeneous condition. This variability contributes to diagnostic challenges and inconsistent responses to therapy. Identifying homogeneous subgroups or phenotypes within ARDS may enhance precision medicine and therapeutic targeting.Objective: This review evaluates the utility of decision tree–based supervised machine learning (ML) algorithms—specifically CART, Random Forest, and AdaBoost—in phenotyping ARDS using clinical, radiological, and biological data.Methods: A comprehensive literature search was conducted between December 2023 and March 2024 using PubMed and Google Scholar. Search terms included ‘decision tree in ARDS’, ‘phenotype in ARDS’, and ‘ML in hypo- and hyperinflammatory ARDS’. Twenty-six relevant articles were included, comprising original studies and reviews.Results: Decision tree–based models have demonstrated significant potential in classifying ARDS subtypes using routine clinical variables, radiographic features, and biomarker profiles. These algorithms have shown strong predictive performance in differentiating inflammatory phenotypes, forecasting mortality, and enabling early ARDS prediction.Conclusion: Decision tree algorithms offer a promising approach to ARDS phenotyping by leveraging routinely available data. Their interpretability and predictive accuracy may aid in translating complex biological insights into bedside clinical decision-making, advancing personalized care in critical illness.

Background: This study explored whether emotional distress, coping, and/or resilience contributed to return to driving (RTD) following experienced mild traumatic brain injury, and whether these variables of interest differed among those who had and had not RTD.  Methods: The present study evaluated de-identified archival data of 65 patients with mTBI following an MVA. Patients were either the driver, passenger, /or pedestrian struck by a motor vehicle, and aged 22 to 69 years. The sample consisted of 36 men and 29 women with an average education. The mean months elapsed between the accident and the assessment was 16.82 months. Pearson correlations were used to test for associations between all explanatory and outcome variables. Separate linear and hierarchical regressions were carried out to evaluate whether variables of interest were significant predictors of RTD.  Results: Findings revealed that the presence of depressive symptoms was associated with coping, irrespective of style, resilience, driving-related anxiety, and RTD. Moreover, RTD was related to driving-related anxiety, too, and in fact, anxiety (considering the presence of depressive symptoms) appeared to be an even greater limiting factor when considering RTD in this population. Age, gender, and education did not influence RTD. Conclusion: The present study revealed that depressive symptoms and driving-related anxiety in particular contribute to whether patients with mTBI RTD, irrespective of time since injury, age, and gender. Coping styles and resilience did not predict RTD. Further work is warranted to address the paucity of research investigating RTD parameters that contribute to and/or hinder RTD among mTBI sufferers.

Hydrogen sulphide [H2S] is created when sewage material breaks down. The well-known "rotten egg" odour is linked to H2S at low quantities. It is a colourless and odourless gas that, at greater quantities, can cause cerebral and respiratory depression, fast unconsciousness, and impending death. As a sad occupational accident, worker deaths in septic tanks or sewage systems are not unusual in medico-legal practice.Death is attributed to poisoning with hazardous gases, particularly hydrogen sulphide, but the depletion of oxygen in the air due to an excess of carbon dioxide is not mentioned. Colleague fatalities are frequently linked to deaths in similar situations. Two tragic accidents that resulted in victim deaths are detailed in this paper. Here, the morphological findings—pulmonary diseases, submucosal/sub-serosal congested haemorrhage, and discolouration from postmortem staining—were discovered in both cases. A detailed scene investigation about the potential for life-threatening H2S poisoning for the assistants, the characteristic rotten egg smell that may be detected on the corpses, and the previously described morphological results should be the foundation for any suspicion of a deadly H2S poisoning. A qualitative and, if feasible, quantitative study of H2S should be performed to confirm the diagnosis.

Y-chromosomal DNA (Y-DNA) testing plays a critical role in forensic investigations involving male suspects, especially when traditional autosomal DNA evidence is insufficient or degraded. This review explores how different environmental factors—such as heat, moisture, Ultraviolet (UV) exposure, and microbial activity—impact the ability to recover Y-DNA from fabrics commonly found at crime scenes, including cotton, polyester, and denim. The study found that longer exposure to harsh environments, especially humidity and UV radiation, led to a sharp drop in the amount and quality of recoverable Y-DNA. The type of fabric also influenced results, with cotton generally retaining more DNA than synthetic materials like polyester. These findings reinforce the need for quick evidence collection and proper storage to preserve the integrity of Y-DNA.  Several real-world cases are highlighted where Y-DNA analysis provided clear forensic outcomes, especially when autosomal DNA failed due to issues like allelic dropout—where one or more genetic markers fail to appear during testing—or secondary transfer, which occurs when DNA is unintentionally passed from one surface or person to another. In such cases, Y-DNA profiling was crucial in narrowing down or identifying male suspects, particularly when other forms of DNA were inconclusive. This review underscores the unique value of Y-DNA analysis in situations involving degraded or limited biological material and calls for the development of better recovery techniques to improve success in challenging forensic contexts.

Advanced forensic approaches are necessary to handle digital crimes, as they must provide transparent methods that foster trust and enable interpretable evidence in judicial investigations. The current black-box machine learning models deployed in traditional digital forensics tools accomplish their tasks effectively yet fail to meet legal standards for admission in court because they lack proper explainability.This study creates an Explainable Artificial Intelligence (XAI) system for digital forensics to improve flagging events as legal evidence by establishing high levels of trust and transparency. A digital evidence system employs interpretable machine learning models together with investigative analysis techniques for the detection and classification of computer-based irregularities, which generate clear explanations of the observed anomalies.The system employs three techniques, including SHAP (Shapley Additive Explanations) alongside LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to deliver understandable explanations about forensic findings, thus enhancing investigation clarity for law enforcement agents and attorneys as well as stakeholder professionals.The system performs successfully on actual digital forensic datasets, thus boosting investigation speed while minimizing false alerts and improving forensic decision explanations. The system must demonstrate GDPR and digital evidence admission framework compliance to maintain legal and ethical correctness for usage in court procedures.Forensic digital investigations need explainable Artificial Intelligence as an essential integration for creating reliable and legally sound practices.

We report the case of a 29-year-old male referred to our surgical department for evaluation of two progressively enlarging lumbar masses with an eight-month history.