The Kakatiya Medical College (KMC) is one of the medical schools of Telangana, located in Warangal under the gamut of Kaloji Narayana Rao University of Health Sciences and the Medical Council of India (MCI).
Sodium–glucose cotransporter 2 inhibitors (SGLT2 inhibitors) and non-steroidal mineralocorticoid receptor antagonists (nsMRAs) exert complementary renoprotective effects in chronic kidney disease (CKD). However, the efficacy and safety of nsMRA plus SGLT2 inhibitor combination therapy compared with SGLT2 inhibitor monotherapy in patients with albuminuria remain uncertain. We systematically searched for randomized controlled trials (RCTs) comparing nsMRA plus SGLT2 inhibitor therapy with SGLT2 inhibitor monotherapy in adults with CKD with albuminuria. Outcomes included changes in albumin-creatinine ratio (ACR), estimated glomerular filtration rate (eGFR), serum potassium, categorical ACR response (≥ 30
Non-alcoholic fatty liver disease (NAFLD) and diabetes mellitus (DM) are increasingly prevalent, interrelated conditions contributing significantly to morbidity and mortality. Understanding long-term mortality trends is essential to guide public health strategies. This study aimed to evaluate nationwide mortality trends involving NAFLD and DM in the United States from 1999 to 2022 and to assess demographic and geographic disparities. CDC WONDER was used to extract data for adults aged ≥ 25 years. 10th Revision (ICD−10), using the explicit codes with K74.0, K74.6, K75.8, K76.0 and K76.9 for NAFLD and E10-E14 for DM. Age- Adjusted Mortality Rates (AAMRs) per million population were calculated and trends were analyzed by calculating annual percent change (APC) and the average annual percent change (AAPC) using Joinpoint Regression Program (version 5.2.0.0; National Cancer Institute), with statistical significance set at p < 0.05. From 1999 to 2022, a total of 126,431 NAFLD and DM related deaths were recorded with most of the deaths occurring in Medical Facilities (43.52
BACKGROUND:Artificial intelligence (AI) is reshaping oncology at every stage of the cancer care pathway, from population-level screening through molecular diagnosis, treatment planning, and post-treatment surveillance. Despite an exponential growth in AI oncology publications exceeding 5000 peer-reviewed studies annually, a critical and persistent gap separates demonstrated algorithmic performance from genuine patient benefit. Most published evidence derives from retrospective, single-institution studies conducted in curated dataset environments that systematically differ from real-world clinical deployment conditions. This comprehensive review examines the translational maturity of AI applications across 18 major malignancies, providing an evidence-stratified, cross-cancer assessment of where AI has fulfilled, approaches, or remains far from fulfilling its transformative potential in oncological care. METHODS:A structured narrative review was conducted across PubMed/MEDLINE, Embase, IEEE Xplore, and the Cochrane Library, supplemented by regulatory grey literature including FDA 510(k) decision summaries, CE Technical Files, and ClinicalTrials.gov. Search terms combined cancer site-specific terminology with AI methodology terms and translational outcome descriptors. Studies were only included if they applied an AI or machine learning methodology to a defined clinical oncological task, reported a clearly specified performance evaluation, and involved human subjects or human-derived clinical data. Evidence quality was assessed using QUADAS-2, PROBAST, and Cochrane RoB 2. A five-tier translational readiness framework, grounded in the NIH T0-T4 translational spectrum and CONSORT-AI/SPIRIT-AI guidelines, was applied a priori to enable cross-cancer comparison. A rigorous distinction was maintained between diagnostic accuracy and clinical utility, defined as demonstrated impact on clinical decision-making or patient-centered outcomes. RESULTS:Across all 18 malignancies, AI development varied profoundly by cancer type. Breast cancer and prostate cancer (Tier 1) represent the most mature AI ecosystems, with multiple FDA-cleared tools for mammographic screening and digital pathology achieving prospective multi-institutional validation; however, randomized evidence demonstrating reduced cancer-specific mortality remains absent. Lung, hepatocellular, and melanoma AI (Tier 2) have achieved regulatory milestones but face documented performance disparities across demographic subgroups, including DermaSensor's 20.7% specificity in primary care settings and HCC model failures in non-viral disease etiologies. Colorectal, glioma, pancreatic, and ovarian cancers (Tier 3) exhibit technical maturity without clinical clarity: colorectal CADe systems increase adenoma detection but meta-analyses of 18,232 patients across 21 RCTs fail to demonstrate improvement in advanced neoplasia detection or cancer incidence reduction. A full study-level presentation of pooled estimates, confidence intervals, and heterogeneity statistics for each cited randomized evidence base across all cancer types would extend beyond the intended scope and format of this cross-cancer narrative review. Gastric, esophageal, cervical, bladder, head and neck, and endometrial cancers (Tier 4) demonstrate promising single-institutional or geographically restricted results without multi-institutional external validation, particularly notable for cervical cancer AI's transformative potential in low- and middle-income countries constrained by absent regulatory frameworks. Hematologic malignancies, sarcoma, and pediatric solid tumors (Tier 5) face structural barriers, workflow incompatibility in hematopathology, extreme rarity in sarcoma (>70 subtypes, <15,000 US cases annually), and irreducible ethical constraints in pediatric data governance, that cannot be resolved through algorithmic refinement alone. CONCLUSIONS:Oncological AI has not yet fulfilled its clinical promise. Across all five translational tiers, a single finding is consistent: diagnostic accuracy is not a surrogate for patient benefit. AI tools with high sensitivity and specificity have repeatedly failed to demonstrate equivalent reductions in cancer-specific mortality, overdiagnosis, or procedural harm under real-world outcome scrutiny. Simultaneously, documented performance disparities across races, ethnicity, disease etiology, and geographic setting reveal that current AI systems risk amplifying the very health inequities they are positioned to resolve. Bridging this translational gap requires three coordinated systemic shifts: regulatory frameworks mandating post-market outcome surveillance as a condition of clinical clearance; prospective trial designs measuring patient-centered endpoints rather than diagnostic concordance alone; and sustained infrastructure investment in federated data governance, demographically inclusive training datasets, and LMIC-accessible regulatory pathways. AI holds genuine potential to reduce cancer mortality on a global scale-but only if held to the evidentiary and equity standards that the stakes of oncological care demand.
Objectives:A higher frequency of valvular heart disease is seen among Systemic Sclerosis (SSc) patients. Advanced aortic valve stenosis leads to significant morbidity in these patients with multiple other comorbidities. In this study, we aim to define the short- and long-term outcomes of transcatheter aortic valve replacement (TAVR) procedures in SSc patients. Methods:We undertook a retrospective chart review of all patients with SSc who underwent a TAVR procedure at our institution over a defined 11-year period. Demographics as well as short- and long-term outcomes were identified. Results:Fourteen SSc patients underwent a TAVR procedure between 2012 and 2023. They were predominantly older Caucasian female patients with limited cutaneous SSc (lcSSc) with advanced aortic stenosis. Only one patient had a readmission within 30 days due to post-op heart failure and subsequently had to have the TAVR reversed to a SAVR. Conclusions:TAVR is a well-tolerated procedure in SSc patients with advanced aortic stenosis and multiple comorbidities.
