Jaslok Hospital and Research Centre is a private hospital located in Mumbai was founded by philanthropist Seth Lokoomal Chanrai along with surgeon Shantilal Jamnadas Mehta. The hospital was formally inaugurated on 6 July 1973 by the erstwhile Prime Minister, Indira Gandhi.The hospital got substantial publicity in the late 1970s when Jayaprakash Narayan was admitted for treatment of kidney failure by the nephrologist M. K. Mani. Narayan died there in 1979.[citation needed]In the early 1980s the laboratories run there by P. R. Krishnaswamy had the first fully automated Kontron biochemistry analyzers, an electron microscope and an aminoacid analyser.[citation needed]The India's first recorded AIDS/HIV victim died at the hospital on 9 June 1986, after a fortnight's treatment.The facility suffered during unrest led by trade unionist Datta Samant. On 17 October 1979, after severing the gas, water and telephone connections and crippling the labour force in the hospital, the management had to shift nearly 294 patients to other hospitals in the city. Dr Rindani, the then Medical Director, stood firm, The police were called in and a court injunction prohibiting assembly of the agitating workers was obtained. Samant had to back down for the first time in his brand of union activities.Jaslok Hospital is located at Dr. G. Deshmukh Marg., Peddar Road, South Mumbai, overlooking the Arabian Sea.The name Jaslok is a combination of the names of Seth Lokoomal and his wife, Jasotibai..
Purpose: This survey aims to provide insights into analyse the association of providing human milk fortification with growth outcomes, early hospital discharge, and the impact on economic outcomes associated with the management of neonates in the NICU setting. Methods: A survey-based analysis was conducted in five hospitals, which were included based on the number of NICU beds, patient volume, and practice of using human milk fortifier for neonates. The survey consisted of 22 questions, which were designed to understand the hospital infrastructure, patient volume, feeding practices for neonates, economic burden, and clinical outcomes in the NICU settings, and was duly filled by the physicians. The average response for each survey question was calculated and further analysed. Results: The survey results report that of all the hospitalized neonates, 75% (7.5/10) are admitted to NICU, and 60% (6/10) receive feeds with human milk fortifier (HMF), while 23% receive unfortified feeds. A higher weight gain of similar to 10-20 gm/kg/d and an estimated 40% lesser days of NICU stay are observed in neonates provided with HMF (approximately 9 days) while neonates receiving unfortified feeds stay for approximately 15 days. Average per day NICU cost for management of a neonate is estimated to be INR 30,000, which amounts to 4.5 lac for 15 days and 2.7 lac for 9 days when provided with unfortified and fortified feeds, respectively, while keeping the costs consistent till discharge. However, the hospitals generally incur a similar to 25-30% higher expense as compared to later days, so, an early discharge can turn into estimated potential savings of 30-40% per month for the hospital. Conclusion: The survey findings suggest utilizing human milk fortifiers during neonatal care in the NICU, may translate into reduced length of stay, and potential health and economic benefits that encompass the baby, the caregiver, and the hospital.
Objective: Minimally invasive nipple-sparing mastectomy (NSM), performed via endoscopic or robotic-assisted approaches, has been developed to improve cosmetic and psychosocial outcomes without compromising oncologic safety. While international experience is growing, data from low-and middle-income countries remain limited. Materials and Methods: We conducted a retrospective case series of five consecutive patients (six breasts) who underwent minimally invasive NSM between January 2024 and June 2025 in an Indian center. Three patients underwent conventional endoscopic NSM and two underwent robotic-assisted NSM (one unilateral and one bilateral). Data collected included demographic and genetic status, tumor biology, operative details, reconstruction method, perioperative complications, pathology, and short-term follow-up. Primary endpoints were feasibility and safety; secondary endpoints were margin status, early oncologic outcomes, and cosmetic satisfaction. Results: All procedures were completed successfully without conversion to open surgery. Median (range) operative time was 210 (180-300) minutes, with robotic procedures requiring longer duration. No intraoperative complications, nipple-areolar necrosis, or implant losses were observed. Two patients developed minor seromas that resolved with aspiration. Pathological margins were negative in all cases. At a median follow-up of six (4-18) months, all patients were alive, disease-free, and reported good-to-excellent cosmetic satisfaction. Conclusion: Our early experience demonstrates that both endoscopic and robotic-assisted NSM are feasible and safe in carefully selected patients, providing satisfactory oncologic and esthetic outcomes. However, these results should be interpreted with caution due to the very small sample size, short follow-up, and absence of a comparator group. Larger prospective multicenter studies with long-term outcomes are required to confirm oncologic safety and define the role of minimally invasive NSM India.
Older adults represent most patients with cancer, yet geriatric expertise remains underrepresented in oncology practice. This review explores practical strategies to integrate geriatricians throughout the continuum of cancer care, from diagnosis to survivorship and palliative care. We discuss models of interdisciplinary collaboration, optimal timing of geriatric involvement, and innovative solutions to address the global shortage of geriatricians. Evidence demonstrates that comprehensive geriatric assessment enhances treatment tolerance, quality of life, and survival among older adults with cancer [1-5]. Despite widespread endorsement, implementation barriers persist due to workforce limitations, time constraints, and lack of institutional support. Emerging strategies include task-shifting, telehealth-enabled assessments, and education programs that empower oncologists and allied health professionals to apply geriatric principles in daily practice [6-10]. Integrating geriatricians into multidisciplinary oncology teams is essential to deliver equitable, personalized, and value-based care for aging populations. We advocate for structured collaboration models, institutional prioritization of geriatric oncology, and policy-level initiatives that reinforce the indispensable role of geriatricians in modern cancer care.
INTRODUCTION:Nosocomial infections (NIs) in cirrhosis are associated with high mortality but could be preventable. Logistic regression (LR) models have failed to identify high-risk patients. We aimed to develop machine learning (ML) models to predict NI. METHODS:The CLEARED consortium consists of prospectively enrolled cirrhosis inpatients from >120 centers. Using day-of-admission clinical data, 3 ML approaches (random forest [RF], extreme gradient boosting, and neural networks [NNs]) were used to predict NI. Data were split 80:20 for training and testing stratified by the outcome. Models were compared using area under the receiver operating characteristic curve (AUC). RESULTS:In total, 8,263 patients (55.90 ± 13.34 years; 64.1% men) from 127 centers in 37 countries were included. NI developed in 869 (10.5%), a median of 6 (4-11) days of postadmission. Major NIs were respiratory (29.6%) and urinary tract infection (15.7%), and spontaneous bacterial peritonitis (13.5%). NIs occurred more frequently in patients from low/low-middle income countries and those with severe liver disease, alcohol etiology, and admission infections. NIs were associated with inpatient mortality (31.9% vs 8.1%, P < 0.001) and liver transplantation (4.8% vs 1.9%, P < 0.001). Although the RF model (AUC 0.69) showed good calibration (Brier score 0.09), outperforming extreme gradient boosting, neural network, and LR models (AUC 0.66 for all; LR comparison P = 0.043), no model achieved AUC ≥0.80 for clinical utility. At 10% predicted probability threshold, the RF model demonstrated only 75.4% sensitivity, 52.9% specificity, and 15.9% positive predictive value (PPV). DISCUSSION:NIs cannot be accurately predicted from day-of-admission data using ML models, even in a large, prospective, global cirrhosis cohort. Every hospitalized patient with cirrhosis should receive protocolized infection control measures.
