Jay Gohri

Cardiac amyloidosis was long considered rare and untreatable, but both assumptions have given way, and the disease is now recognized as an underdiagnosed and increasingly treatable cause of heart failure. The available therapies work by slowing amyloid deposition, so their benefit depends on early diagnosis, yet the condition is still identified late and usually only after referral for specialized imaging. Artificial intelligence has started to shorten this path. Models built on the electrocardiogram and the echocardiogram detect amyloid infiltration with high accuracy and have reached external validation, while artificial-intelligence-based analysis of heart sound, the cheapest and most widely available cardiac signal, has not yet been directly evaluated in this disease. The physiological rationale for studying it is strong, because a ventricle stiffened by amyloid is expected to alter the low-frequency diastolic sounds that reflect ventricular compliance, although whether these changes are specific to amyloidosis remains unknown. This review brings together the acoustic manifestations of cardiac amyloidosis, the signal-processing and machine learning methods relevant to heart sounds, the clinical evidence across imaging and acoustic modalities, and the practical and equity questions that deployment raises. Heart sound analysis remains the most scalable and least explored opportunity in the field. We outline the mechanistic rationale, the available methods, the lessons from the more mature electrocardiographic and echocardiographic literature, and a set of minimum design requirements for a first disease-specific study.

Giant cell arteritis (GCA) is the most common primary systemic vasculitis of older adults and has traditionally been framed as an ophthalmologic emergency. Its most serious consequences, however, occur in the cardiovascular system, often years after inflammatory markers have normalized. In contemporary matched population cohorts, patients with GCA carry an approximately two-fold excess risk of aortic aneurysm; the historical Olmsted County cohort reported a 17-fold excess of thoracic aortic aneurysm with wide confidence limits, and the characteristic latency to aneurysm is five to ten years. Patients also face elevated risks of aortic dissection, myocardial infarction, stroke, venous thromboembolism, and heart failure that cluster within the first year of diagnosis, when vascular inflammation is most active. This narrative review, based on a structured search of PubMed/MEDLINE and Google Scholar through September 2026 with independent verification of every cited estimate, synthesizes current evidence on the full spectrum of cardiac and aortic complications of GCA, including aortitis, aneurysm and dissection, coronary arteritis, valvular disease, pericarditis, myocarditis, and conduction disease. We trace their common origin to the immunopathology of the arterial wall, where dendritic cell activation, Th1/Th17 polarization, and maladaptive vascular remodeling produce occlusion in medium-sized arteries and dilation in the aorta. We examine current diagnostic modalities, from the ultrasound-based fast-track clinic, which has reduced permanent visual loss by half or more in observational cohorts, to FDG-PET/CT, in which aortic uptake at diagnosis is associated with subsequent aortic dilation. We review treatment after the GiACTA and SELECT-GCA trials in the light of the 2025 European Alliance of Associations for Rheumatology (EULAR) recommendations, the unresolved role of antiplatelet and statin therapy, and the surgical and endovascular management of GCA-related aortopathy. We suggest that the organization of care, as well as the limitations of drug therapy, may contribute to the residual mortality of GCA, a hypothesis that has not been tested; distinguish recommendations that are supported by current guidelines from those that are our own proposals; and outline a cardio-rheumatology pathway in which baseline vascular phenotyping informs individualized aortic surveillance. GCA is a disease whose natural history often begins in the rheumatology clinic and ends in the cardiac operating room without prior surveillance.

Hypertension has been traditionally known to be highlighted by mean blood pressure; however, emerging evidence exhibits that blood pressure variability (BPV), including short-term, day-to-day, and visit-to-visit fluctuations can have an implication across multiple body systems. Elevated BPV reflects repetitive hemodynamic stress, affecting the physiologic hemostasis contributing to vascular injury and end organ damage. This narrative review is a compilation of recent evidence on the prognostic value of BPV, explained by pathophysiology, various devices with its measurement approaches, and, essentially, the clinical implication of BPV and the use of such devices utilizing artificial intelligence. A comprehensive literature search across PubMed, Cochrane Library, Scopus, and Web of Science were conducted, focusing on observational studies, cohorts, randomized trials, and meta-analyses. Higher BPV has been associated with an increased risk of cardiovascular mortality, stroke, coronary events, and heart failure, the progression of chronic kidney disease, cognitive decline, and preeclampsia, among other end organ damage, despite mean blood pressure. The various pathophysiologic mechanisms include autonomic dysregulation, arterial stiffness, endothelial dysfunction, circadian rhythm alteration, and systemic inflammation, which result in vascular remodeling and multisystem damage. Antihypertensive medications such as calcium channel blockers and renin–angiotensin–aldosterone system inhibitors seem to reduce BPV; randomized trials have not specifically investigated their BPV-reducing effects. The aim of this review is to highlight that BPV is a dynamic marker of multisystem risk, and question how various AI-based devices can aid continuous BPV monitoring and patient specific risk stratification.

Phonoenterography (PEG) offers a non-invasive and radiation-free technique to assess gastrointestinal activity through acoustic signal analysis. In this feasibility study, 110 high-resolution PEG recordings (44.1 kHz, 16-bit) were acquired from eight healthy individuals, yielding 6314 prominent bowel sound (PBS) segments through automated segmentation. Each event was characterized using a 279-feature acoustic profile comprising Mel-frequency cepstral coefficients (MFCCs), their first-order derivatives (Δ-MFCCs), and six global spectral parameters. After normalization and dimensionality reduction with PCA and UMAP (cosine distance, 35 neighbors, minimum distance = 0.01), five clustering strategies were evaluated. K-Means (k = 5) achieved the most favorable balance between cluster quality (silhouette = 0.60; Calinski–Harabasz = 19,165; Davies–Bouldin = 0.68) and interpretability, consistently identifying five acoustic patterns: single-burst, multiple-burst, harmonic, random-continuous, and multi-modal. Temporal modeling of clustered events further revealed distinct sequential dynamics, with Single-Burst events showing the longest dwell times, random continuous the shortest, and strong diagonal elements in the transition matrix confirming measurable state persistence. Frequent transitions between random continuous and multi-modal states suggested dynamic exchanges between transient and overlapping motility patterns. Together, these findings demonstrate that unsupervised PEG-based analysis can capture both acoustic variability and temporal organization of bowel sounds. This annotation-free approach provides a scalable framework for real-time gastrointestinal monitoring and holds potential for clinical translation in conditions such as postoperative ileus, bowel obstruction, irritable bowel syndrome, and inflammatory bowel disease.

Empathy is vital in healthcare, influencing patient care quality and outcomes. Understanding the factors that affect empathy levels among healthcare professionals is essential for improving healthcare delivery. This systematic review aims to identify and analyze the factors influencing empathy levels among healthcare professionals, including demographic and professional characteristics. We conducted a systematic search across multiple databases, yielding 979 studies. After removing 116 duplicates, 863 records were screened based on titles and abstracts. Following eligibility criteria, 140 articles were retrieved for full-text assessment, of which 129 were excluded. Ultimately, nine studies were included in the meta-analysis. Data were synthesized to evaluate associations between empathy levels and factors such as gender, marital status, age, educational level, and job characteristics. The meta-analysis revealed significant associations: females exhibited higher empathy levels compared to males [standardized mean difference (SMD): −0.18; 95% confidence interval (CI): −0.32 to −0.04], and married individuals showed greater empathy than unmarried individuals (SMD: −0.30; 95% CI: −0.43 to −0.16). Age did not significantly influence empathy levels (SMD: 0.13; 95% CI: −0.16 to 0.41). No significant differences were found in empathy scores between nurses and other healthcare professionals or between doctors and nurses. Gender and marital status are significant predictors of empathy levels among healthcare professionals, while age and specialty do not appear to significantly impact empathy. These findings highlight the need for targeted interventions to enhance empathetic practices in healthcare settings.

Accurate diagnosis of gastrointestinal (GI) diseases typically requires invasive procedures or imaging studies that pose the risk of various post-procedural complications or involve radiation exposure. Bowel sounds (BSs), though typically described during a GI-focused physical exam, are highly inaccurate and variable, with low clinical value in diagnosis. Interpretation of the acoustic characteristics of BSs, i.e., using a phonoenterogram (PEG), may aid in diagnosing various GI conditions non-invasively. Use of artificial intelligence (AI) and improvements in computational analysis can enhance the use of PEGs in different GI diseases and lead to a non-invasive, cost-effective diagnostic modality that has not been explored before. The purpose of this work was to develop an automated AI model, You Only Listen Once (YOLO), to detect prominent bowel sounds that can enable real-time analysis for future GI disease detection and diagnosis. A total of 110 2-minute PEGs sampled at 44.1 kHz were recorded using the Eko DUO^® stethoscope from eight healthy volunteers at two locations, namely, left upper quadrant (LUQ) and right lower quadrant (RLQ) after IRB approval. The datasets were annotated by trained physicians, categorizing BSs as prominent or obscure using version 1.7 of Label Studio Software^®. Each BS recording was split up into 375 ms segments with 200 ms overlap for real-time BS detection. Each segment was binned based on whether it contained a prominent BS, resulting in a dataset of 36,149 non-prominent segments and 6435 prominent segments. Our dataset was divided into training, validation, and test sets (60/20/20% split). A 1D-CNN augmented transformer was trained to classify these segments via the input of Mel-frequency cepstral coefficients. The developed AI model achieved area under the receiver operating curve (ROC) of 0.92, accuracy of 86.6%, precision of 86.85%, and recall of 86.08%. This shows that the 1D-CNN augmented transformer with Mel-frequency cepstral coefficients achieved creditable performance metrics, signifying the YOLO model’s capability to classify prominent bowel sounds that can be further analyzed for various GI diseases. This proof-of-concept study in healthy volunteers demonstrates that automated BS detection can pave the way for developing more intuitive and efficient AI-PEG devices that can be trained and utilized to diagnose various GI conditions. To ensure the robustness and generalizability of these findings, further investigations encompassing a broader cohort, inclusive of both healthy and disease states are needed.

Cutis laxa is a genetically heterogeneous disorder characterized primarily by loose, redundant skin with abnormal wrinkling and elasticity. It is an exceptionally rare condition, with an estimated prevalence of < 1 in 1,000,000 individuals. In addition to the distinctive cutaneous manifestations, cutis laxa can present with a constellation of other features, including progeroid appearance, growth retardation, and developmental delays. We report a case of a 26‐month‐old girl who presented with features similar to nutritional rickets with global developmental delay and some additional features of joint and skin hyper‐laxity in the backdrop of severely low vitamin D levels. The patient, however, failed to respond to the conventional treatment for rickets. Subsequent genetic testing revealed an autosomal dominant form of cutis laxa caused by an exceedingly rare c.377G>A (p.Arg126His) substitution in the ALDH18A1 gene, which encodes the bifunctional enzyme catalyzing the final steps of de novo phospholipid biosynthesis. The present case highlights the diagnostic challenges posed by cutis laxa, as the clinical manifestations can overlap with other conditions, leading to potential misdiagnosis or delayed recognition. The rarity of this disorder, combined with its phenotypic variability, underscores the importance of raising awareness among clinicians and expanding the literature to encompass the full spectrum of presentations associated with cutis laxa.

Covid-19, caused by SARS-CoV-2, is known for its diverse systemic effects, though rheumatological and cutaneous manifestations are relatively uncommon. We report a striking case of a middle-aged Caucasian woman who developed a diffuse, bilateral rash that progressed to gangrenous lesions. Despite testing positive for SARS-CoV-2, she exhibited no respiratory symptoms. After excluding thromboembolic events and other vasculitides, a skin biopsy confirmed leukocytoclastic vasculitis, leading to a diagnosis of COVID-19-associated leukocytoclastic vasculitis. The extensive gangrenous progression ultimately required bilateral below-knee amputation, despite medical management. While mild vasculitic presentations have been observed in COVID-19 patients, progression to such severe gangrenous lesions is extremely rare. This case underscores the potential for devastating cutaneous complications in SARS-CoV-2 infections, highlighting the need for heightened awareness and further research into the pathogenic mechanisms of COVID-19-related vasculopathies.