Sharma & Associates (United States)

Generative AI is rapidly transforming enterprise operations, yet its applicability to cybersecurity beyond automation remains limited. This paper presents a strategic framework to embed generative AI within the reengineering of cybersecurity processes, transitioning from reactive and compliance-driven paradigms to proactive, quality-managed systems. We discuss how generative AI can enhance the translation of complex frameworks, improve stakeholder alignment, and accelerate continuous improvement. Through technical applications and real-world examples, we demonstrate that generative AI can elevate cybersecurity maturity, optimize workflows, and bolster strategic resilience.

Chemotherapy preparation and administration is a complex nursing procedure. Adequate competency and positive behaviors regarding safe handling of cytotoxic drugs is very important for every nurse to ensure patient safety as well as occupational safety. This study assessed the perceived barriers and risks regarding safe handling of chemotherapeutic drugs among nursing personnel of a tertiary care hospital of Delhi, India. A descriptive cross-sectional design was conducted among 60 nursing personnel, who were working in chemotherapy wards and day care units. Self-administered structured questionnaire and rating scales were used for data collection. The sociodemographic and outcome variables were analyzed using descriptive statistics in addition to inferential statistics. The overall mean scores of practice, perceived barriers, and risks of nursing personnel toward safe handling of chemotherapy is 33.26±3.18, 29.75±4.66, and 11.75±2.99, respectively. The most important barrier was inadequate training on chemotherapy and high workload. The highest risk perceived by the nursing personnel was an inadequate regular medical surveillance program, followed by immediate non-replacement of linens soiled with drug spills. It is recommended that chemotherapy safety protocol, safety surveillance systems, and in-service training be instituted for all nursing personnel who are working in an oncology unit.

These are simulation snapshots (.pdb) from classical MD of complex I from <em>Yarrowia lipolytica.</em> The file names correspond to setups as described in https://doi.org/10.1002/1873-3468.14346 These files can be visualised in software such as VMD or Pymol. The subunits used in these snapshots: pfrag 0, chain D, segname APPD<br> pfrag 1, chain G, segname APPG<br> pfrag 2, chain 9, segname APP9<br> pfrag 3, chain W, segname APPW<br> pfrag 4, chain U, segname APPU<br> pfrag 5, chain P, segname APAP<br> pfrag 6, chain H, segname APAH<br> pfrag 7, chain Z, segname APAZ<br> pfrag 8, chain F, segname APAF<br> pfrag 9, chain E, segname APAE<br> pfrag 10, chain I, segname CHA9<br> pfrag 11, chain B, segname CHA6<br> pfrag 12, chain 5, segname CHA5<br> pfrag 13, chain 4, segname CHA4<br> pfrag 14, chain 8, segname CHAH<br> pfrag 15, chain A, segname CHAA<br> pfrag 16, chain J, segname CHAJ<br> pfrag 17, chain K, segname CHAK

Introduction/Hypothesis: To develop and validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine noncontrast chest CTs and evaluate for outcomes prognosis. Methods: A prototype AI algorithm, with a conditional variational auto-encoder architecture, was initially developed and trained using training data from 1313 patients (paired CTA and non-contrasted exams; “training group”). Left atrium contours from contrasted cases were used to train the model on corresponding non-contrasted dataset. We then retrospectively evaluated 274 patients (mean age 67.9 years, 54.6% male, 57.2% caucasian) who underwent a routine non ECG-gated, noncontrast chest CT for coronary artery calcium or lung cancer screening for validation of the AI model (“validation group”). Outcomes included heart failure (HF) hospitalization and new-onset atrial fibrillation within 5 years. Simple logistic regressions with appropriate univariate statistics were used for modelling outcomes. Results: In the validation group, there was excellent correlation between AI and expert results with a LA volume index (LAVi) intraclass correlation coefficient of 0.950 (0.936-0.960). Bland-Altman plot demonstrated that the AI underestimated LAVi by a mean 5.86 mL/m2. AI-LAVi independently predicted new-onset atrial fibrillation (AUC = 0.855, OR = 1.12, 95% CI 1.08 - 1.18; p&lt;0.0001). AI-LAVi was independently associated with HF hospitalization (AUC = 0.903, OR = 1.08, 95% CI 1.04 - 1.13, p = 0.0009). Conclusion: In this large validation cohort, AI for automated measurement of LA volume on routine noncontrast chest CTs had excellent agreement with manual quantification. AI-LAVi is independently predictive for new-onset atrial fibrillation and HF hospitalization within 5 years.

Background: Speed of infarct progression varies widely in acute ischemic stroke and is a major determinant of outcome. Patient demographic, clinical, and imaging features associated with slow, intermediate, and rapid infarct growth have not been well delineated. Methods: In a prospectively maintained stroke center registry, we analyzed consecutive patients with anterior circulation large vessel occlusion who underwent first multimodal MRI or CT imaging within 24 hours of onset. The speed of initial infarct progression was calculated as ischemic core volume at first imaging divided by the time from stroke onset to imaging. Results: Among the 88 patients, age was mean 71.6 ± 15.0; 51% were women; initial NIHSS was 16.1 ± 6.5), and time from onset to first imaging was median 3.3h (full range 0.6 - 23.0). The speed of infarct progression was median 2.2 cc/hr (interquartile range 0 - 8.7), ranging most widely among patients imaged within the first 6 hours after onset. Faster speed of infarct progression was positively independently associated with low collateral score (OR 3.30, 95%CI 1.25 - 10.49) and arrival by emergency medical services rather than transfer (OR 3.34, 95% CI 1.06 - 10.49) and negatively independently associated with prior ischemic stroke (OR 0.12, 95%CI 0.03 - 0.50) and coronary artery disease (OR 0.32, 95%CI 0.10 - 1.00). Among the 67 patients who underwent endovascular thrombectomy, slower speeds of infarct progression were associated with shift to reduced levels of disability at discharge (OR 3.26, 95% CI 1.02 - 10.45). In addition, slower speed of infarct progression was associated with favorable shift to recanalization by thrombectomy (OR 8.30, 95%CI 0.97 - 70-.87) and reduced radiologic hemorrhagic transformation (OR 0.34, 95% CI 0.12 - 0.94). Conclusion: Slower speed of initial infarct progression is associated with high collateral score, prior ischemic stroke, and coronary artery disease, supporting roles for both collateral robustness and ischemic precondition in fostering tissue resilience to ischemia. Among patients undergoing endovascular thrombectomy, speed of initial infarct progression is a major determinant of clinical outcome.

Life-cycle cost analyses are often considered in bridge management in conjunction with the decision-making process for bridge maintenance, rehabilitation, or replacement. An accurate estimate for these costs requires a better understanding, and proper modeling of, deterioration rate of a bridge's components throughout its useful time. In considering the deterioration rate in bridge life-cycle cost analysis and management, special attention will need to be paid in regard to the potential for accelerated fatigue damage that can occur because of frequent use of overloads. Overloads and their potential for fatigue damage may in fact become a major cost item in the bridge overall life-cycle cost. In this study, an index representing the fatigue life expended (FLE) due to overloads is introduced and used as a driving factor in the deterioration curve to incorporate the significance of fatigue damage from overloads in the decision-making strategies in maintenance and rehabilitation and in bridge management.

Background: Prior studies have suggested race-ethnic differences in functional outcome, mortality, and use of comfort care after stroke, but have been limited by analysis of single stroke subtypes, lack of 3-month outcomes, and uncertainty whether delayed access to acute care was moderating response patterns. Methods: We analyzed all patients with final diagnoses of acute cerebral ischemia or intracranial hemorrhage enrolled within 2 hours of onset in the multicenter, prehospital NIH FAST-MAG trial. Self- or family-reported race-ethnicity was analyzed for relation to presenting stroke features, stroke subtype, and 3-month outcome. Results: Among 1616 hyperacute cerebrovascular disease patients (76.4% acute cerebral ischemia, 23.6% intracranial hemorrhage), race-ethnic frequencies were: white, non-Hispanic (W) - 55.5%, Hispanic (H) - 23.1%, Black (B) - 13.6%, Asian (A) - 8.4%. Ischemic vs hemorrhagic stroke differed by race-ethnicity, with hemorrhagic strokes more common in Hispanics (34.0%) and Asians (28.1%), compared with Whites (19.8%) and Blacks (17.8%), p = 0.0001. Age at time of stroke was highest in Whites (mean 73.1), vs H (65.5), B (63.5), A (66.9); and presenting severity (NIHSS) highest in A (mean 13.5) and H (12.7), vs W (10.7) and B (10.8). A greater dimorphic spread in 3 month outcomes was seen in W than in the other race-ethnic groups: Whites had more excellent (mRS 0-1) 3 month outcomes - W- 42.2%, B- 35.0%, H -28.4%, A-28.0%, p = 0.002; but Whites also had higher mortality by 3m - W-18.2%, A- 15.6%, H-13.0%, B-10.6%, p = 0.007. The race-ethnic difference in mortality was driven by hemorrhagic rather than ischemic strokes: ICH mortality - W-41.7%, A-19.4%, H-19.0%, B-18.2%; IS mortality: A-14.4%, W-13.5%, H-10.5%, B-9.4%. Conclusions: In this multicenter study of acute cerebrovascular disease in a diverse US region, notable race-ethnic differences included higher frequencies of hemorrhagic vs ischemic stroke among Hispanics and Asians; and more excellent, but also more fatal, 3 month outcomes among Whites. These patterns likely distinct different race-ethnic risk factor profiles shaping incident stroke type, as well as divergent sociocultural influences upon robust rehabilitation care and comfort care measures.

Background: Age is a major determinant of stroke outcome, but few studies have investigated whether the effect of age differs between men and women. Methods: Analysis of all patients enrolled in a multicenter acute stroke trial (FAST-MAG) presenting with acute cerebral ischemia and treated without intravenous thrombolysis. Effects of age and sex were evaluated for: 1) disability or death (modified Rankin Scale, mRS, 2-6); 2) reduced stroke-related quality of life (Stroke Impact Scale, SIS &lt; 80); and 3) mortality, unadjusted and adjusted for baseline NIHSS, ASPECTS, and 7 additional prognostic variables. Results: Among the 772 patients meeting entry criteria, age was 71.2, 44.0% were women, and presenting NIHSS 6.8. Overall, men were slightly younger than women (69.9 vs 73.0), without difference in initial NIHSS (6.4 vs 7.2), nor in 3m outcomes: mRS 2-6, 44.7% vs 50.2%, p=0.13; SIS&lt;80, 44.6% vs 44.1%, p=0.90; mortality 9.5% vs 9.7%, p=0.92. However, the sexes differed in age-functional outcome patterns. Among women, there were U-shaped relationships between age and rates of both mRS 2-6 (Figure) and SIS &lt;80. In contrast, among men, a much more attenuated U-shaped relationship was visualized, did not reach statistical significance. For both sexes, mortality showed a generally linear increase with age. Compared to those aged 40-64, mortality rates in the oldest old (age ≥85) were: women - HR 4.81 (95CI 0.98-23.39); men - HR 4.06 (95CI 1.17-14.14). Conclusions: The relationship between age and functional outcome in supportively treated acute cerebral ischemia patients differs between the sexes, with women but not men showing better outcomes in early senescence than either midlife or very late-life. This contrasting outcome profile may reflect sex-specific biological differences in ischemic stroke mechanism and neuroplasticity, cultural differences in social roles and support, or additional factors that merit further study.

Critical derailment incidents associated with crude oil and ethanol transport have led to a renewed focus on improving the performance of tank cars against the potential for puncture under derailment conditions. Proposed strategies for improving accident performance have included design changes to tank cars, as well as, operational considerations such as reduced speeds. In prior publications, the authors have described the development of a novel methodology for quantifying and characterizing the reductions in risk that result from changes to tank car designs or the tank car operating environment. The methodology considers key elements that are relevant to tank car derailment performance, including variations in derailment scenarios, chaotic derailment dynamics, nominal distributions of impact loads and impactor sizes, operating speed differences, and variations in tank car designs, and combines these elements into a consistent framework to estimate the relative merit of proposed mitigation strategies. The modeling approach involves detailed computer simulations of derailment events, for which typical validation techniques are difficult to apply. Freight train derailments are uncontrolled chain events, which are prohibitively expensive to stage and instrument; and their chaotic nature makes the unique outcome of each event extremely sensitive to its particular set of initial and bounding conditions. Furthermore, the purpose of the modeling was to estimate the global risk reduction expected in the U.S. from tank car derailments, not to predict the outcome of a specific derailment event. These challenges call into question which validation techniques are most appropriate, considering both the modeling intent as well the availability and fidelity of the data sets available for validation. This paper provides an overview of the verification and validation efforts that have been used to enhance confidence in this methodology.

Background: Heart failure with reduced ejection fraction (HFrEF) has a significant impact on patients’ health status. A primary goal of treatment is to minimize patients’ symptoms, while maximizing their functional status and quality of life. The variability across outpatient practices, and the characteristics of those practices, in optimizing patients’ health status is unknown. Methods: In the multicenter CHAMP-HF registry, the association between practice characteristics and Kansas City Cardiomyopathy Questionnaire Overall Summary Scores (KCCQ-os) in 3,494 patients across 140 US centers was assessed with hierarchical logistic regression adjusting for 23 patient characteristics to predict excellent health status (KCCQ-os ≥75). A median odds ratio (MOR) was estimated to quantify the average relative difference a patient would exhibit excellent health status when being treated at one random practice versus another. Results: The mean (±standard deviation) KCCQ-os was 64.2±24, and 39% (n=1,380) had a KCCQ-os ≥75. In a fully-adjusted model, patients were more likely to have excellent health status if the practice was in a suburban (vs. urban) location (OR=1.47, p = 0.02), while those enrolled at a primary care (vs. cardiology) setting were less likely to have a KCCQ≥75 (OR=0.52, p = 0.02). The MOR across practices was 1.69 (95% CI=1.46, 1.90; p &lt; 0.0001) indicating a median 69% higher odds of a statistically identical patient having excellent health status when treated at one random practice versus another. Conclusion: In this first large, contemporary assessment of health status in outpatients with HFrEF, we found marked variation by site in patients having excellent health status. Proposed efforts to use patient-reported outcomes as a quality measure may support improved attention to patients’ health status in clinical care.