Search

Lung adenocarcinoma (LUAD), the most prevalent form of non-small cell lung cancer (NSCLC), remains a leading cause of cancer-related death globally, including in India, with a 5-year survival rate below 10%. Despite these grim statistics, recent advances in the use of next-generation sequencing (NGS) for identifying genetic alterations and the emergence of targeted therapies have opened new possibilities for personalized treatment based on distinct molecular signatures. To understand the molecular pattern of NSCLC, a retrospective study was conducted with 53 Indian LUAD patient samples, using a targeted NGS panel of 46 cancer-relevant oncogenes to identify clinically relevant variants. Pathogenic or likely pathogenic variants were detected in 94% of the 53 cases. Non-synonymous mutations, rearrangements, copy number alterations, insertions, and deletions of functional relevance were observed in 31 out of 46 genes. The most frequently mutated genes included TP53 (52.8%) and EGFR (50.9%), followed by RET, PIK3CA and ERBB2; some patients had multiple alterations in the same gene. Gender-based enrichment analysis indicated that ALK and IDH2 alterations were more prevalent in females, while TP53 and PTEN were more common in males. No significant correlation was found between mutations and other clinicopathological attributes, such as age, stage, and subtype. A higher prevalence of EGFR, RET, PIK3CA, ERBB2 and ALK mutations were observed compared to previous LUAD genetic studies coupled with a lower frequency of KRAS mutations. Clinically actionable variants were annotated using OncoKB and categorized into the four therapeutic levels based on their clinical evidence. Seventy-nine percent of cases had at least one clinically actionable alteration. Most patients (39.6%) had the highest level of actionability (Level 1) wherein an FDA-approved drug is available specifically for the observed mutation in lung cancer patients. EGFR Exon19 in-frame deletions and EGFR L858R were the most frequent among targetable variants (20.7%). These findings emphasize the importance of a selective NGS panel in enabling personalized medicine approaches by identifying actionable molecular alterations and informing the choice of targeted therapy for more effective treatment options in Indian NSCLC patients.

Background Accountable care organizations (ACOs) aim to improve health care quality and reduce costs, including among patients with heart failure (HF). However, variation across ACOs in admission rates for patients with HF and associated factors are not well described. Methods and Results We identified Medicare fee‐for‐service beneficiaries with HF who were assigned to a Medicare Shared Savings Program ACO in 2017 and survived ≥30 days into 2018. We calculated risk‐standardized acute admission rates across ACOs, assigned ACOs to 1 of 3 performance categories, and examined associations between ACO characteristics and performance categories. Among 1 232 222 beneficiaries with HF, 283 795 (mean age, 81 years; 54% women; 86% White; 78% urban) were assigned to 1 of 467 Medicare Shared Savings Program ACOs. Across ACOs, the median risk‐standardized acute admission rate was 87 admissions per 100 people, ranging from 61 (minimum) to 109 (maximum) admissions per 100 beneficiaries. Compared to the overall average, 13% of ACOs performed better on risk‐standardized acute admission rates, 72% were no different, and 14% performed worse. Most ACOs with better performance had fewer Black beneficiaries and were not hospital affiliated. Most ACOs that performed worse than average were large, located in the Northeast, had a hospital affiliation, and had a lower proportion of primary care providers. Conclusions Admissions are common among beneficiaries with HF in ACOs, and there is variation in risk‐standardized acute admission rates across ACOs. ACO performance was associated with certain ACO characteristics. Future studies should attempt to elucidate the relationship between ACO structure and characteristics and admission risk.

Abstract Background Disorders involving the musculoskeletal system are often identified with short stature and a range of orthopedic problems. The clinical and genetic heterogeneity of these diseases along with several characteristic overlaps makes definitive diagnosis difficult for clinicians. Hence, using molecular testing in addition to conventional tests becomes essential for appropriate diagnosis and management. Methods Comprehensive clinical examination, detailed pretest and posttest counseling, molecular diagnosis with next-generation sequencing (NGS), genotype–phenotype correlation and Sanger sequencing for targeted variant analysis. Results This manuscript reports a molecular spectrum of variants in 34 orthopedic cases referred to a single genetic unit attached to a tertiary care hospital. The diagnostic yield of NGS-based tests coupled with genetic counseling and segregation analysis was 79% which included 7 novel variants. In about 53% (i.e. 18/34 cases), molecular testing outcome was actionable since 8 of the 18 underwent prenatal diagnosis, as they were either in their early gestation or had planned a pregnancy subsequent to molecular testing, while ten cases were premaritally/prenatally counseled for the families to take informed decisions as they were in the reproductive age. Conclusions The report highlights the importance of NGS-based tests even in a low resource setting as it helps patients, families and healthcare providers in reducing the economic, social and emotional burden of these disorders.

Abstract Recently, the finite state machine (FSM)‐based digital controllers are susceptible to fault‐injection and side‐channel attacks, which makes FSM security a more prominent factor. FSM optimality is another crucial element when designed. A state encoding approach is applied for FSM security and optimization. This article proposes the security‐dominated FSM state assignment technique (SD‐SHO), which obtains a satisfactory level of FSM optimization as well. It is a deterministic algorithm and consists of two key techniques, such as state assignment using an improved quadratic sum code, and state assignment using a gradient‐based interior point method. A fuzzy bi‐level programming logic is introduced in the proposed approach for regulating the constituting algorithms optimally. Experiments are conducted to evaluate the FSM security and optimality using the MCNC FSM benchmarks. Results indicate a substantial reduction in the error masking probability using SD‐SHO. It also demonstrates that SD‐SHO achieves a satisfactory level of area and power reduction compared with other existing works.

Abstract Objective To develop and validate a clinical risk prediction score for noninvasive ventilation (NIV) failure defined as intubation after a trial of NIV in non-surgical patients. Design Retrospective cohort study of a multihospital electronic health record database. Patients Non-surgical adult patients receiving NIV as the first method of ventilation within two days of hospitalization. Measurement Primary outcome was intubation after a trial of NIV. We used a non-random split of the cohort based on year of admission for model development and validation. We included subjects admitted in years 2010–2014 to develop a risk prediction model and built a parsimonious risk scoring model using multivariable logistic regression. We validated the model in the cohort of subjects hospitalized in 2015 and 2016. Main results Of all the 47,749 patients started on NIV, 11.7% were intubated. Compared with NIV success, those who were intubated had worse mortality (25.2% vs. 8.9%). Strongest independent predictors for intubation were organ failure, principal diagnosis group (substance abuse/psychosis, neurological conditions, pneumonia, and sepsis), use of invasive ventilation in the prior year, low body mass index, and tachypnea. The c-statistic was 0.81, 0.80 and 0.81 respectively, in the derivation, validation and full cohorts. We constructed three risk categories of the scoring system built on the full cohort; the median and interquartile range of risk of intubation was: 2.3% [1.9%–2.8%] for low risk group; 9.3% [6.3%–13.5%] for intermediate risk category; and 35.7% [31.0%–45.8%] for high risk category. Conclusions In patients started on NIV, we found that in addition to factors known to be associated with intubation, neurological, substance abuse, or psychiatric diagnoses were highly predictive for intubation. The prognostic score that we have developed may provide quantitative guidance for decision-making in patients who are started on NIV.

Background In randomized controlled trials, perioperative administration of amiodarone has been shown to reduce the incidence of postoperative atrial arrhythmias and length of stay (LOS) among patients undergoing coronary bypass surgery. However, little is known about the use or effectiveness of perioperative amiodarone in routine clinical practice. Methods and Results We studied patients ≥18 years old without a previous history of atrial or ventricular arrhythmias who underwent elective coronary bypass surgery between 2013 and 2014 within a network of 235 US hospitals. Perioperative amiodarone was defined as receipt of amiodarone either on the day of or the day preceding surgery. We used covariate‐adjusted modeling and instrumental variable methods to examine the association between receipt of amiodarone and the development of atrial arrhythmias, in‐hospital mortality, readmission, LOS, and cost. Of 12 758 patients, 2195 (17.2%) received perioperative amiodarone, 3330 (26.1%) developed atrial arrhythmias postoperatively, and the average LOS was 6.4 days (±2.6 days). Instrumental variable analysis showed that receipt of perioperative amiodarone was associated with lower risk of atrial arrhythmias (risk difference −11 percentage points, 95% CI −19 to −4 percentage points; P=0.002) and a shorter LOS (−0.7 day, 95% CI −1.39 to −0.01 days; P=0.048). There was no association between receipt of perioperative amiodarone and in‐hospital mortality, cost, or readmission. Conclusions Among patients undergoing coronary bypass surgery without previous arrhythmias, perioperative amiodarone is associated with a lower risk of atrial arrhythmias and shorter LOS. These findings are consistent with previous randomized trials and lend support to current guideline recommendations.

Recently, the Reconfigurable FSM has drawn the attention of the researchers for multistage signal processing applications. The optimal synthesis of Reconfigurable finite state machine with input multiplexing (Reconfigurable FSMIM) architecture is done by the iterative greedy heuristic based Hungarian algorithm (IGHA). The major problem concerning IGHA is the disintegration of a state encoding technique. This paper proposes the integration of IGHA with the state assignment using logarithmic barrier function based gradient descent approach to reduce the hardware consumption of Reconfigurable FSMIM. Experiments have been performed using MCNC FSM benchmarks which illustrate a significant area and speed improvement over other architectures during field programmable gate array (FPGA) implementation.

We present a pixel-level angle sensitive detector composed of silicon-on-insulator (SOI) photodiode (PD) stacked with a gold surface plasmon (SP) antenna to affect the direction of the incoming light. The surface plasmons are excited in the grating-type SP antenna and enhance the diffraction efficiency of the grating. The diffracted light is coupled strongly with the propagation light in the SOI waveguide when the phase matching condition is satisfied. The phase matching takes place at a specific angle of light incidence, and the discrimination of the light based on the incident angle is achieved. As spatial patterns in the polar coordinate of the elevation-azimuth angles (θ, ϕ) of the incident light, we present the phase matching condition theoretically, the absorption efficiency in the SOI by simulation, and also the quantum efficiency of the SOI PD experimentally for different SP antennas of one-dimensional (1D) line-and-space (L/S) and two-dimensional (2D) hole array gratings under various polarization angles. 1D grating offers a polarization sensitive angle detection and 2D grating exhibits angle detection in two orthogonal directions, enabling a polarization independent angle sensitivity. A good agreement among the theory, simulation, and experiment are attained. The proposed device features relatively high quantum efficiency as an angle-sensitive pixel (ASP) and gives wider opportunities in applications such as three-dimensional (3D) imaging, depth-of-field extension, and lensless imaging.

Background Little is known about the safety of nicotine replacement therapy (NRT) in smokers hospitalized with coronary heart disease. Methods and Results We examined the short‐term safety of NRT use among smokers hospitalized for coronary heart disease in a geographically and structurally diverse sample of US hospitals in the year 2014. We compared smokers who started NRT in the first 2 days of hospitalization with smokers without any exposure to NRT and adjusted for baseline differences through propensity score matching. Outcomes included inpatient mortality, hospital length of stay, and 1‐month readmission. From 270 hospitals, we included 27 459 smokers (mean age, 58 years; 69% men; 56.9% in intensive care unit), of whom 4885 (17.8%) received NRT (97.2% used the nicotine patch, at a median dose of 21 mg/d for 3 days). After propensity matching, covariates were well balanced within each patient group. Among patients with myocardial infarction, compared with patients who did not receive NRT, those who received NRT showed no difference in mortality (2.1% versus 2.3%; P=0.98), mean length of stay (4.4±3.5 versus 4.3±3.3 days; P=0.60), or 1‐month readmission (15.8% versus 14.6%; P=0.31). Results were similar for patients undergoing percutaneous coronary intervention or coronary artery bypass surgery. Conclusions Among smokers hospitalized for treatment of coronary heart disease, use of NRT was not associated with any differences in short‐term outcomes. Given the known beneficial effects of NRT in treating nicotine withdrawal, reducing cravings, and promoting smoking cessation after discharge, our findings suggest that NRT is a safe and reasonable treatment option.

Background Although models exist for predicting hospital readmission after coronary artery bypass surgery, no such models exist for predicting readmission after heart valve surgery (HVS). Methods and Results Using a geographically and structurally diverse sample of US hospitals (Premier Inpatient Database, January 2007–June 2011), we examined patient, hospital, and clinical factors predictive of short‐ and medium‐term hospital readmission post‐HVS. We set aside 20% of hospitals for model validation. A generalized estimating equation model accounted for clustering within hospitals. At 219 hospitals, we identified 38 532 patients (67 years, 56% male, 62% aortic valve surgery) who underwent HVS. A total of 3125 (7.8%) and 4943 (12.8%) patients were readmitted to the index hospital within 1 and 3 months, respectively. Our 3‐month model predicted readmission rates between 3% and 61% with fair discrimination (C‐statistic, 0.67) and good calibration (predicted vs observed differences in validation cohort averaged 1.9% across all deciles of predicted readmission risk). Results were similar for our 1‐month model and our simplified 3‐month model (suitable for clinical use), which used the 5 strongest predictors of readmission: transfused units of packed Red blood cells, presence of End‐stage renal disease, type of Valve surgery, Emergency hospital admission, and hospital Length of stay (REVEaL). Conclusions We described and validated key factors that predict short‐ and medium‐term hospital readmission post‐HVS. These models should enable clinicians to identify individuals with HVS who are at increased risk for hospital readmission and are most likely to benefit from improved postdischarge care and follow‐up.

BACKGROUND:Mortality prediction models generally require clinical data or are derived from information coded at discharge, limiting adjustment for presenting severity of illness in observational studies using administrative data. OBJECTIVES:To develop and validate a mortality prediction model using administrative data available in the first 2 hospital days. RESEARCH DESIGN:After dividing the dataset into derivation and validation sets, we created a hierarchical generalized linear mortality model that included patient demographics, comorbidities, medications, therapies, and diagnostic tests administered in the first 2 hospital days. We then applied the model to the validation set. SUBJECTS:Patients aged ≥ 18 years admitted with pneumonia between July 2007 and June 2010 to 347 hospitals in Premier, Inc.'s Perspective database. MEASURES:In hospital mortality. RESULTS:The derivation cohort included 200,870 patients and the validation cohort had 50,037. Mortality was 7.2%. In the multivariable model, 3 demographic factors, 25 comorbidities, 41 medications, 7 diagnostic tests, and 9 treatments were associated with mortality. Factors that were most strongly associated with mortality included receipt of vasopressors, non-invasive ventilation, and bicarbonate. The model had a c-statistic of 0.85 in both cohorts. In the validation cohort, deciles of predicted risk ranged from 0.3% to 34.3% with observed risk over the same deciles from 0.1% to 33.7%. CONCLUSIONS:A mortality model based on detailed administrative data available in the first 2 hospital days had good discrimination and calibration. The model compares favorably to clinically based prediction models and may be useful in observational studies when clinical data are not available.

The twin image-free phase reconstruction is still a challenge with single-shot inline holographic systems. Existing solutions mostly are based on the inverse problem approaches or alternating projections. However, there exists a trade-off between phase retrieval and twin image elimination. Recent studies have introduced a hybrid method involving both the approaches to mitigate this trade-off. Following these works, we propose a single-shot sparsity-assisted iterative phase retrieval approach that applies a sparsity constraint in the object domain and formulates phase retrieval as a minimization problem. We demonstrate lensless digital inline holographic microscopy for imaging transparent and weakly scattering biological samples over a large field-of-view of ∼ 29 mm 2. The proposed method achieves high fidelity phase reconstruction with faster convergence compared to the existing single-shot phase retrieval methods. We further demonstrate the phase quantification of label-free biological samples, such as cervical cells and RBCs, to highlight the potential of our technique in clinical applications. [ABSTRACT FROM AUTHOR]