The Coronavirus disease 2019 pandemic caused by Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to the rise of many available modalities for diagnosis. One such modality is the Reverse Transcription–Polymerase Chain Reaction (RT-PCR) kits which require evaluation amongst the many available commercial kits in the market. We conducted a performance evaluation of twelve RT‐PCR SARS‐CoV‐2 commercial kits. A total of 75 nasopharyngeal and oropharyngeal clinical samples were selected with their cycling threshold (Ct) values. Inclusion of 5 gene targets: E gene, N gene, S gene, RdRp and ORF1ab were assessed. Data was analyzed using R software version 4.1.1 and Microsoft Excel We observe that, the positive sample's Ct values differs significantly across the 12 diagnostic kits. However, for gene-specific analysis, we observe that, positive sample's Ct values does not differ significantly across gene targets. There is significant difference in Ct values in Commercial kits targeting all genes except S-gene. All the commercial kits Altona (E and S genes), Thermo (ORF1ab and N genes), Multiplex (E, ORF1ab, RdRdp genes), Meril (N and ORF1ab genes), S D Biosensor (E and ORF1ab genes), Lab Gun (RdRp and N genes) and Lab systems (ORF1ab and E genes) scored a sensitivity of 100%. All other kits scored sensitivity above 95% and lowest sensitivity with the Genes2me (E gene) and Genes2me (RdRp) at 95.08% each. All kits were 100% specific. This study provides an accurate comprehensive assessment of the different kits in the detection of SARS-CoV-2 which may promote standardization of testing across laboratories.

Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and its management is limited by heterogeneous risk profiles, suboptimal surveillance performance, diagnostic uncertainty in chronically diseased livers, and difficulty individualizing prognosis after treatment. The aim of this narrative review was to critically evaluate artificial intelligence (AI) applications across the HCC care continuum, with emphasis on their intended clinical role, reported performance, evidence maturity, and barriers to implementation. A major strength of this review is that it moves beyond a descriptive catalog of models by structuring the literature around clinically relevant decision points and by explicitly distinguishing emerging proof-of-concept tools from applications with stronger translational potential. Across risk stratification, surveillance, imaging-based diagnosis, pathology, treatment-response prediction, and prognostication, we found that AI consistently demonstrates promise, particularly for identifying patients at higher future HCC risk, improving lesion detection and characterization on ultrasound, CT, MRI, and contrast-enhanced ultrasound, assisting histopathologic classification, and predicting outcomes such as microvascular invasion, recurrence, survival, and response to locoregional therapies. However, we also found that the evidence base remains highly uneven: many diagnostic studies are retrospective and lesion-enriched rather than embedded in true surveillance populations, many prognostic models lack robust external validation and calibration assessment, and reference standards, imaging protocols, and dataset composition vary substantially across studies. These findings are clinically relevant because they highlight both where AI may offer near-term value and why most published systems are not yet ready for routine use. Overall, AI in HCC should be viewed as a rapidly evolving but still transitional field. Its future impact will depend not only on higher-performing algorithms but on clearly defined clinical use cases, multicenter and prospective validation, transparent reporting, workflow-aware evaluation, and implementation strategies that support safe, equitable, and scalable adoption.

Diabetes insipidus (DI) is a disorder of water hemostasis that is associated with polyuria-polydipsia syndrome. Central DI (CDI) primarily results from autoimmune destruction, traumatic injury, or anatomical damage caused by neoplasms. Craniopharyngioma, germinoma, and distant metastases are the main neoplastic causes, with pituitary adenomas rarely manifesting as CDI. Pituitary gland metastasis is rare, with the vast majority of cases being asymptomatic. We present a rare case of pituitary metastasis originating from small-cell carcinoma of the lung with CDI and skin swellings as the primary manifestation, without any evidence of the primary malignancy upon initial presentation. A 56-year-old chronic smoker with newly diagnosed type-2 diabetes mellitus presented with a history of polydipsia and polyuria along with soft tissue swellings in the axilla and the chest for the last 3 months. A water deprivation test and a desmopressin challenge test were performed, revealing the presence of CDI. In light of the CDI, a contrast-enhanced magnetic resonance imaging brain was performed, which displayed a loss of pituitary bright spot and four T2 isointense lesions with post-contrast enhancement in the left frontal, parietal, occipital, and right temporal lobes, suggestive of metastatic lesions. Fine needle aspiration cytology of the swelling revealed cytomorphological characteristics indicating the presence of malignancy, specifically favoring carcinoma. Contrast-enhanced computed tomography thorax revealed a right hilar lung mass infiltrating the surrounding structures with multiple regional and distant metastases. A lung biopsy confirmed the presence of small-cell lung carcinoma (SCLC). The final diagnosis was advanced SCLC with multiple distant metastases associated with CDI, and the patient is currently receiving palliative care and inhalational desmopressin. In conclusion, metastatic lesions and lung cancer must be considered early when patients present with polydipsia and polyuria symptoms.

Background: Current artificial intelligence (AI) systems in gastrointestinal (GI) endoscopy primarily emphasize binary detection or static classification, providing limited support for the graded assessment of malignant potential that underpins clinical decision-making. We developed GastroMalign, a transformer-based framework designed to stratify GI lesions according to ordinal disease severity while maintaining clinical interpretability, addressing this unmet need in endoscopic risk assessment. Methods: This retrospective development and validation study used the publicly available GastroVision dataset, comprising 8000 de-identified endoscopic still images from the upper and lower gastrointestinal tract, including the esophagus, stomach, duodenum, colon, rectum, and terminal ileum. GastroMalign integrates a Vision Transformer (ViT) encoder with a Sequential Feature Learner that explicitly models ordinal disease severity along a benign-to-malignant spectrum. The framework produces both categorical risk classification and a continuous malignancy risk score. Images were stratified into training (80%), validation (10%), and test (10%) sets. Performance was compared with convolutional neural network (CNN) baselines and a Swin Transformer. Interpretability was assessed using Score-CAM visualizations reviewed by blinded expert endoscopists. Results: On the held-out test set (n = 800 images), GastroMalign achieved an overall accuracy of 80.06%, precision of 79.65%, recall of 80.06%, and F1-score of 79.17%, with a micro-averaged AUC of 0.98. In comparison, ResNet-50 and DenseNet-121 achieved accuracies of 32.42% and 36.77%, respectively, while the Swin Transformer achieved 60.56% accuracy (AUC = 0.93). Ablation analyses demonstrated a 17% absolute reduction in High-Risk lesion recall when the progression-aware module was removed. Continuous malignancy risk scores increased monotonically across ordinal classes, with mean values < 0.18 for Benign and >0.72 for High-Risk/Malignant lesions. Score-CAM visualizations demonstrated 92% overlap with clinician-annotated lesion regions. Conclusions: GastroMalign delivers an interpretable, progression-aware AI framework for GI lesion risk stratification that outperforms existing CNN- and transformer-based models. Clinically, GastroMalign is intended as an adjunct decision-support tool during endoscopic review to standardize lesion risk stratification (benign to malignant spectrum), support management decisions (biopsy vs. resection vs. surveillance), and reduce operator-dependent variability by pairing ordinal risk outputs with interpretable visual explanations.