Simultaneous estimation of paclitaxel and bortezomib via LC-MS/MS: pharmaceutical and pharmacokinetic applications.

Aim & Objective: This study evaluates the potential of combining paclitaxel (PTX) and bortezomib (BTZ) for breast cancer therapy. Materials & Methods: The nanoformulation was optimized via Box-Behnken Design (BBD), with method validation adhering to US-FDA guidelines. Results: Multiple reaction monitoring transitions for PTX, BTZ and internal standard were m/z 855.80→286.60, 366.80→226.00 and 179.80→110.00, respectively. Elution done on C18 Luna column with 0.1% FA in MeOH:10 mM ammonium acetate. The size of nanoformulation was 133.9 ± 1.97 nm, PDI 0.19 ± 0.01 and zeta potential -19.20 ± 1.36 mV. Pharmacokinetics showed higher Cmax for PTX-BTZ-NE (313.75 ± 10.71 ng/ml PTX, 11.92 ± 0.53 ng/ml BTZ) versus free PTX-BTZ (104 ± 13.06 ng/ml PTX, 1.9 ± 0.08 ng/ml BTZ). Conclusion: Future findings will contribute to the treatment of breast cancer using PTX and BTZ.

[Box: see text].

Restoring initial steps by intermittent theta burst stimulation in complete spinal cord injury patient: a case report.

INTRODUCTION: Spinal cord injury (SCI) causes damage to neurons and results in motor and sensory dysfunction. Intermittent theta burst stimulation (iTBS) has been used to induce neuronal and synaptic plasticity by applying a magnetic field in the brain. The plasticity induced in the cortex has an imperative role in the recovery of motor and sensory functioning. However, the effect of iTBS in complete SCI patients is still elusive. CASE PRESENTATION: We report here the case of a 27-year-old female who sustained an L1 complete spinal cord injury (SCI) with an ASIA score of A. The patient lost all the sensory and motor functions below the level of injury. Intermittent theta burst stimulation (iTBS) was administered at 80% of the resting motor threshold over the M1 motor cortex, along with intensive rehabilitation training to promote sensorimotor function. DISCUSSION: There was a partial recovery in functional, electrophysiological, and neurological parameters. The case report also demonstrates the safety and efficacy of iTBS in complete SCI patients. No adverse event has been observed in the patient during intervention sessions.

Circulating Metabolite Profiles and Risk of Coronary Heart Disease Among Racially and Geographically Diverse Populations.

BACKGROUND: Metabolomics may reveal novel biomarkers for coronary heart disease (CHD). We aimed to identify circulating metabolites and construct a metabolite risk score (MRS) associated with incident CHD among racially and geographically diverse populations. METHODS: Untargeted metabolomics was conducted using baseline plasma samples from 900 incident CHD cases and 900 age-/sex-/race-matched controls (300 pairs of Black Americans, White Americans, and Chinese adults, respectively), which detected 927 metabolites with known identities among ≥80% of samples. After quality control, 896 case-control pairs remained and were randomly divided into discovery (70%) and validation (30%) sets within each race. In the discovery set, conditional logistic regression and least absolute shrinkage and selection operator over 100 subsamples were applied to identify metabolites robustly associated with CHD risk and construct the MRS. The MRS-CHD association was evaluated using conditional logistic regression and the C-index. Mediation analysis was performed to examine if MRS mediated associations between conventional risk factors and incident CHD. The results from the validation set were presented as the main findings. RESULTS: Twenty-four metabolites selected in ≥90% of subsamples comprised the MRS, which was significantly associated with incident CHD (odds ratio per 1 SD, 2.21 [95% CI, 1.62-3.00] after adjusting for sociodemographics, lifestyles, family history, and metabolic health status). MRS could distinguish incident CHD cases from matched controls (C-index, 0.69 [95% CI, 0.63-0.74]) and improve CHD risk prediction when adding to conventional risk factors (C-index, 0.71 [95% CI, 0.65-0.76] versus 0.67 [95% CI, 0.61-0.73]; P<0.001). The odds ratios and C-index were similar across subgroups defined by race, sex, socioeconomic status, lifestyles, metabolic health, family history, and follow-up duration. The MRS mediated large portions (46.0%-74.2%) of the associations for body mass index, smoking, diabetes, hypertension, and dyslipidemia with incident CHD. CONCLUSIONS: In a diverse study sample, we identified 24 circulating metabolites that, when combined into an MRS, were robustly associated with incident CHD and modestly improved CHD risk prediction beyond conventional risk factors.

Dissecting the Interplay Mechanism among Process Parameters toward the Biofabrication of High-Quality Shapes in Embedded Bioprinting.

Embedded bioprinting overcomes the barriers associated with the conventional extrusion-based bioprinting process as it enables the direct deposition of bioinks in 3D inside a support bath by providing in situ self-support for deposited bioinks during bioprinting to prevent their collapse and deformation. Embedded bioprinting improves the shape quality of bioprinted constructs made up of soft materials and low-viscosity bioinks, leading to a promising strategy for better anatomical mimicry of tissues or organs. Herein, the interplay mechanism among the printing process parameters toward improved shape quality is critically reviewed. The impact of material properties of the support bath and bioink, printing conditions, cross-linking mechanisms, and post-printing treatment methods, on the printing fidelity, stability, and resolution of the structures is meticulously dissected and thoroughly discussed. Further, the potential scope and applications of this technology in the fields of bioprinting and regenerative medicine are presented. Finally, outstanding challenges and opportunities of embedded bioprinting as well as its promise for fabricating functional solid organs in the future are discussed.

An UltraMNIST classification benchmark to train CNNs for very large images.

Current convolutional neural networks (CNNs) are not designed for large scientific images with rich multi-scale features, such as in satellite and microscopy domain. A new phase of development of CNNs especially designed for large images is awaited. However, application-independent high-quality and challenging datasets needed for such development are still missing. We present the 'UltraMNIST dataset' and associated benchmarks for this new research problem of 'training CNNs for large images'. The dataset is simple, representative of wide-ranging challenges in scientific data, and easily customizable for different levels of complexity, smallest and largest features, and sizes of images. Two variants of the problem are discussed: standard version that facilitates the development of novel CNN methods for effective use of the best available GPU resources and the budget-aware version to promote the development of methods that work under constrained GPU memory. Several baselines are presented and the effect of reduced resolution is studied. The presented benchmark dataset and baselines will hopefully trigger the development of new CNN methods for large scientific images.

Cortical intermittent theta burst stimulation on gait pathomechanics and urinary tract dysfunction in incomplete spinal cord injury patients: Protocol for a randomized controlled trial.

Gait impairment and neurogenic bladder are co-existing common findings in incomplete spinal cord injury (iSCI). Repetitive transcranial magnetic stimulation (rTMS), evident to be a promising strategy adjunct to physical rehabilitation to regain normal ambulation in SCI. However, there is a need to evaluate the role of Intermittent theta burst stimulation (iTBS), a type of patterned rTMS in restoring gait and neurogenic bladder in SCI patients. The aim of the present study is to quantify the effect of iTBS on spatiotemporal, kinetic, and kinematic parameters of gait and neurogenic bladder dyssynergia in iSCI. After maturing all exclusion and inclusion criteria, thirty iSCI patients will be randomly divided into three groups: Group-A (sham), Group-B (active rTMS) and Group-C (active iTBS). Each group will receive stimulation adjunct to physical rehabilitation for 2 weeks. All patients will undergo gait analysis, as well assessment of bladder, electrophysiological, neurological, functional, and psychosocial parameters. All parameters will be assessed at baseline and 6th week (1st follow-up). Parameters except urodynamics and gait analysis will also be assessed after the end of the 2 weeks of the intervention (post-intervention) and at 12th week (2nd follow-up). Appropriate statistical analysis will be done using various parametric and non-parametric tests based on results.

Ensuring safety and efficacy in combination products: regulatory challenges and best practices.

Combination products, amalgamating drugs, biologics, and medical devices, have revolutionized the healthcare landscape with their potential for innovative therapies. However, the intersection of diverse components within these products presents a complex regulatory environment, demanding rigorous attention to safety and efficacy. This article delves into the intricate landscape of regulatory considerations, safety, and efficacy assessments pertaining to combination products-a category at the intersection of drugs, devices, and biologics. The regulatory framework, primarily governed by the U.S. Food and Drug Administration (FDA), necessitates a nuanced classification determining the regulatory pathway. Collaboration between diverse regulatory centers, such as the Center for Drug Evaluation and Research (CDER) and the Center for Devices and Radiological Health (CDRH), underscores the integrated approach required for these innovative healthcare solutions. Safety considerations unravel the potential risks and adverse events associated with combining diverse components, emphasizing the need for robust risk assessment and mitigation strategies. The evaluation of efficacy involves sophisticated methodologies, clinical trials, and post-market surveillance, with recent advancements incorporating digital technologies. This comprehensive exploration aims to contribute to the evolving understanding and best practices in the regulatory and scientific realms, fostering collaboration and innovation in the development and assessment of combination products.

Adaptive Neural Message Passing for Inductive Learning on Hypergraphs.

Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes and are not designed for encoding the higher-order relations. This drawback is mitigated by hypergraphs, in which an edge can connect an arbitrary number of nodes. Most hypergraph learning approaches convert the hypergraph structure to that of a graph and then deploy existing geometric deep learning methods. This transformation leads to information loss, and sub-optimal exploitation of the hypergraph's expressive power. We present HyperMSG, a novel hypergraph learning framework that uses a modular two-level neural message passing strategy to accurately and efficiently propagate information within each hyperedge and across the hyperedges. HyperMSG adapts to the data and task by learning an attention weight associated with each node's degree centrality. Such a mechanism quantifies both local and global importance of a node, capturing the structural properties of a hypergraph. HyperMSG is inductive, allowing inference on previously unseen nodes. Further, it is robust and outperforms state-of-the-art hypergraph learning methods on a wide range of tasks and datasets. Finally, we demonstrate the effectiveness of HyperMSG in learning multimodal relations through detailed experimentation on a challenging multimedia dataset.

Associations of neighborhood sociodemographic environment with mortality and circulating metabolites among low-income black and white adults living in the southeastern United States.

BACKGROUND: Residing in a disadvantaged neighborhood has been linked to increased mortality. However, the impact of residential segregation and social vulnerability on cause-specific mortality is understudied. Additionally, the circulating metabolic correlates of neighborhood sociodemographic environment remain unexplored. Therefore, we examined multiple neighborhood sociodemographic metrics, i.e., neighborhood deprivation index (NDI), residential segregation index (RSI), and social vulnerability index (SVI), with all-cause and cardiovascular disease (CVD) and cancer-specific mortality and circulating metabolites in the Southern Community Cohort Study (SCCS). METHODS: The SCCS is a prospective cohort of primarily low-income adults aged 40-79, enrolled from the southeastern United States during 2002-2009. This analysis included self-reported Black/African American or non-Hispanic White participants and excluded those who died or were lost to follow-up ≤ 1 year. Untargeted metabolite profiling was performed using baseline plasma samples in a subset of SCCS participants. RESULTS: Among 79,631 participants, 23,356 deaths (7214 from CVD and 5394 from cancer) were documented over a median 15-year follow-up. Higher NDI, RSI, and SVI were associated with increased all-cause, CVD, and cancer mortality, independent of standard clinical and sociodemographic risk factors and consistent between racial groups (standardized HRs among all participants were 1.07 to 1.20 in age/sex/race-adjusted model and 1.04 to 1.08 after comprehensive adjustment; all P < 0.05/3 except for cancer mortality after comprehensive adjustment). The standard risk factors explained < 40% of the variations in NDI/RSI/SVI and mediated < 70% of their associations with mortality. Among 1110 circulating metabolites measured in 1688 participants, 134 and 27 metabolites were associated with NDI and RSI (all FDR < 0.05) and mediated 61.7% and 21.2% of the NDI/RSI-mortality association, respectively. Adding those metabolites to standard risk factors increased the mediation proportion from 38.4 to 87.9% and 25.8 to 42.6% for the NDI/RSI-mortality association, respectively. CONCLUSIONS: Among low-income Black/African American adults and non-Hispanic White adults living in the southeastern United States, a disadvantaged neighborhood sociodemographic environment was associated with increased all-cause and CVD and cancer-specific mortality beyond standard risk factors. Circulating metabolites may unveil biological pathways underlying the health effect of neighborhood sociodemographic environment. More public health efforts should be devoted to reducing neighborhood environment-related health disparities, especially for low-income individuals.

Clay-polymer nanocomposites for effective water treatment: opportunities, challenges, and future prospects.

The metal intoxication and its associated adverse effects to humans have led to the research for development of water treatment technologies from pollution hazards. Therefore, development of cheaper water remediation technologies is more urgent than ever. Clays and clay minerals are naturally occurring, inexpensive, non-toxic materials possessing interesting chemical and physical properties. As a result of interesting surface properties, these have been developed as efficient absorbent in water remediation. Recently, clay-polymer nanocomposites have provided a cost-effective technological platform for removing contaminants from water. Covering research advancements from past 25 years, this review highlights the developments in clay-polymer nanocomposites and their advanced technical applications are evaluated with respect to the background and issues in remediation of toxic metals and organic compounds from water. The extensive analysis of literature survey of more than two decades suggests that future work need to highlight on advancement of green and cost-effective technologies. The development of understanding of the interaction and exchange between toxin and clay-polymer composites would provide new assembly methods of nanocomposites with functional molecules or nanomaterials need to be extended to increase the detection and extraction limit to parts per trillion.

Cognitive driven gait freezing phase detection and classification for neuro-rehabilitated patients using machine learning algorithms.

BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate. RESEARCH QUESTION: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation. METHOD: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition. RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers. SIGNIFICANCE: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.

A Versatile Photocrosslinkable Silicone Composite for 3D Printing Applications.

Embedded printing has emerged as a valuable tool for fabricating complex structures and microfluidic devices. Currently, an ample of amount of research is going on to develop new materials to advance its capabilities and increase its potential applications. Here, we demonstrate a novel, transparent, printable, photocrosslinkable, and tuneable silicone composite that can be utilized as a support bath or an extrudable ink for embedded printing. Its properties can be tuned to achieve ideal rheological properties, such as optimal self-recovery and yield stress, for use in 3D printing. When used as a support bath, it facilitated the generation microfluidic devices with circular channels of diameter up to 30 µm. To demonstrate its utility, flow focusing microfluidic devices were fabricated for generation of Janus microrods, which can be easily modified for multitude of applications. When used as an extrudable ink, 3D printing of complex-shaped constructs were achieved with integrated electronics, which greatly extends its potential applications towards soft robotics. Further, its biocompatibility was tested with multiple cell types to validate its applicability for tissue engineering. Altogether, this material offers a myriad of potential applications (i.e., soft robotics, microfluidics, bioprinting) by providing a facile approach to develop complicated 3D structures and interconnected channels.

Novel EGFR mutations in diffuse midline gliomas using cost-effective strategies: A report of 2 cases.

Background: Diffuse midline gliomas (DMGs) are malignant tumors predominantly affecting children, often leading to poor outcomes. The 2021 World Health Organization classification identifies 3 subtypes of DMGs, all characterized by the loss of H3K27 trimethylation. Here, we report 2 cases of DMG with Epidermal Growth Factor Receptor (EGFR) mutations within exon 20, contributing to the understanding of the molecular complexity of these pediatric brain tumors. Methods: An economical immunohistochemical panel was designed to aid in the diagnosis of most DMGs in resource-constrained regions. Sanger sequencing was employed to identify rare EGFR mutations in exon 20 of 2 cases. Results: Molecular analyses of 2 cases of DMG revealed novel EGFR mutations within exon 20. These mutations were identified using cost-effective diagnostic approaches. The presence of EGFR mutations expands the molecular landscape of DMGs and highlights the genetic heterogeneity within this tumor entity. Conclusions: These findings underscore the molecular heterogeneity of DMGs and the significance of identifying novel mutations, such as EGFR mutations in exon 20. Further research into the molecular mechanisms underlying DMGs is warranted to advance therapeutic strategies and improve outcomes for pediatric patients.

The chronic use of serotonin norepinephrine reuptake inhibitors facilitates dyskinesia priming in early Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) patients are frequently exposed to antidepressant medications (ADMs). Norepinephrine (NE) and serotonin (5HT) systems have a role in levodopa-induced dyskinesias (LID) pathophysiology. METHODS: We performed a longitudinal analysis on the PPMI cohort including drug-naïve PD patients, who are progressively exposed to dopamine replacement therapies (DRTs) to test the effect of ADM exposure on LID development by the 4th year of follow-up. RESULTS: LID prevalence (according to MDS UPDRS score 4.1 ≥ 1) was 16% (42/251); these patients were more likely women (p = 0.01), had higher motor (p < 0.001) and depression scores (p = 0.01) and lower putaminal DAT binding ratio (p = 0.01). LID were associated with the exposure time to L-DOPA (2.2 ± 1.07 vs 2.6 ± 0.9, p = 0.02) and to the exposure to ADMs, in particular to SNRI (4.8% vs 21.4%, p < 0.001). The latter persisted after correcting for significant covariates (e.g., disease duration, cognitive status, motor impairment, depression, dopaminergic denervation). A similar difference in LID prevalence in PD patients exposed vs non-exposed to SNRI was observed on matched data by the real-world TriNetX repository (22% vs 13%, p < 0.001). DISCUSSION: This study supports the presence of an effect of SNRI on LID priming in patients with early PD. Independent prospective cohort studies are warranted to further verify such association.

Re-Admissions of 'Unknown' Traumatic Brain Injury Patients - Inadequacy of Rehabilitative Services in a Developing Country.

BACKGROUND: In neurosurgical practice, continuous care after discharge and the ability to detect subtle indicators of clinical deterioration are mandatory to prevent the progression of a disease. The care of 'unknown' patients discharged to rehabilitation homes may not have this privilege, especially in resource-poor countries such as India. OBJECTIVE: We have attempted to study the causes and outcomes of re-admissions of 'unknown' patients with previous traumatic brain injury (TBI) to estimate the quality of nursing care in our rehabilitation centers. MATERIAL AND METHODS: The electronic hospital records of all consecutive 'unknown' TBI patients with unplanned re-admissions at our institute from January 2014 to December 2018 were retrospectively reviewed and analyzed for the factors determining the risk and outcomes of re-admission. RESULTS: Out of 245 patients sent to rehabilitation homes at discharge, 47 patients (19.18%) were re-admitted. A total of 33 patients (70%) were re-admitted between 1 month and 1 year. Out of these, 38 patients (80.9%) were re-admitted because of preventable causes. Fifteen patients (31.9%) died during the hospital stay. The rest of the 32 (68%) patients were discharged after the management of the concerned condition with an average hospital stay of 9 ± 11.1 days. The average Glasgow coma scale (GCS) at re-admission of the patients who died was 6 (range 3-11). Two patients were brought in the brain dead status, whereas 20 patients (42.6%) had a GCS of 5 or below at the time of re-admission. The risk of mortality among patients with non-preventable causes was 88.9% (8/9) compared to preventable causes 18.4% (7/38). However, preventable causes for re-admission are much more common, resulting in nearly a similar overall contribution to mortality. CONCLUSIONS: There is a high rate of mortality and morbidity in 'unknown' patients with TBI because of poor post-discharge care in developing countries. Because preventable causes are the major contributor to re-admissions, the re-admission rate is a good indicator of a lack of adequate rehabilitative services. The need for improving the post-discharge management of 'unknown' patients with TBI in resource-poor countries cannot be over-emphasized.

Blood Lipids, Lipoproteins, and Apolipoproteins With Risk of Coronary Heart Disease: A Prospective Study Among Racially Diverse Populations.

BACKGROUND: Comprehensive blood lipoprotein profiles and their association with incident coronary heart disease (CHD) among racially and geographically diverse populations remain understudied. METHODS AND RESULTS: We conducted nested case-control studies of CHD among 3438 individuals (1719 pairs), including 1084 White Americans (542 pairs), 1244 Black Americans (622 pairs), and 1110 Chinese adults (555 pairs). We examined 36 plasma lipids, lipoproteins, and apolipoproteins, measured by nuclear magnetic resonance spectroscopy, with incident CHD among all participants and subgroups by demographics, lifestyle, and metabolic health status using conditional or unconditional logistic regression adjusted for potential confounders. Conventionally measured blood lipids, that is, total cholesterol, triglycerides, low-density lipoprotein-cholesterol, and high-density lipoprotein-cholesterol, were each associated with incident CHD, with odds ratios (ORs) being 1.33, 1.32, 1.24, and 0.79 per 1-SD increase among all participants. Seventeen lipoprotein biomarkers showed numerically stronger associations than conventional lipids, with ORs per 1-SD among all participants ranging from 1.35 to 1.57 and a negative OR of 0.78 (all false discovery rate <0.05), including apolipoprotein B100 to apolipoprotein A1 ratio (OR, 1.57 [95% CI, 1.45-1.7]), low-density lipoprotein-triglycerides (OR, 1.55 [95% CI, 1.43-1.69]), and apolipoprotein B (OR, 1.49 [95% CI, 1.37-1.62]). All these associations were significant and consistent across racial groups and other subgroups defined by age, sex, smoking, obesity, and metabolic health status, including individuals with normal levels of conventionally measured lipids. CONCLUSIONS: Our study highlighted several lipoprotein biomarkers, including apolipoprotein B/ apolipoprotein A1 ratio, apolipoprotein B, and low-density lipoprotein-triglycerides, strongly and consistently associated with incident CHD. Our results suggest that comprehensive lipoprotein measures may complement the standard lipid panel to inform CHD risk among diverse populations.

A Novel Spider Monkey Optimization for Reliable Data Dissemination in VANETs Based on Machine Learning.

The growth in linked and autonomous vehicles has led to the emergence of vehicular ad hoc networks (VANETs) as a means to enhance road safety, traffic efficiency, and passenger comfort. However, VANETs face challenges in facilitating trustworthiness and high-quality services due to communication delays caused by traffic, dynamic topology changes, variable speeds, and other influencing factors. Hence, there is a need for a reliable data dissemination scheme capable of reducing communication delays among hops by identifying effective forwarder nodes. In this paper, we propose a novel, weighted, estimated, spider monkey-based, nature-inspired optimization (w-SMNO) method to generate a set of efficient relays. Additionally, we introduce a dynamic weight assignment and configuration model to enhance system accuracy using a neural network based on backpropagation with gradient descent optimization techniques to minimize errors in the machine learning model. The w-SMNO also incorporates a distinct algorithm for effective relay selection among multiple monkey spider groups. The simulation results demonstrate substantial improvements in w-SMNO, with a 35.7% increase in coverage, a 41.2% reduction in the end-to-end delay, a 36.4% improvement in the message delivery rate, and a 38.4% decrease in the collision rate compared to the state-of-the-art approaches.