-
Alternative Therapies in Health and... May 2024To establish and determine the content of the genotoxic impurity piperidine in the active pharmaceutical ingredient (API) of rimonabant using a liquid...
OBJECTIVE
To establish and determine the content of the genotoxic impurity piperidine in the active pharmaceutical ingredient (API) of rimonabant using a liquid chromatography-mass spectrometry (LC-MS) method. This study underscores the importance of detecting piperidine due to its potential health risks, including carcinogenic and mutagenic effects, thus highlighting the critical need for rigorous quality control in pharmaceutical products.
METHODS
An Atlantis C18 column (5 μm, 3.9×100 mm) was chosen for separation due to its high efficiency and selectivity for piperidine, with a gradient elution of 0.05% formic acid-water (A) and methanol (B) as the mobile phase at a flow rate of 1.0 mL/min. The column temperature was optimized at 30°C to ensure peak resolution and sensitivity, the injection volume was set to 5.0 μL to minimize sample consumption while maintaining detectability, and the analysis time was kept at 7 min for efficient throughput.
RESULTS
Piperidine demonstrated excellent linearity in the concentration range of 0.03-0.40 μg/mL (R>0.99), with a detection limit of 0.01010 µg/mL. This detection limit is significantly lower than regulatory thresholds, indicating the method's high sensitivity compared to existing methods and its adequacy for regulatory compliance in pharmaceutical quality control.
CONCLUSION
This LC-MS method not only demonstrated high accuracy, good repeatability, and strong durability but also sets a benchmark for future research, regulatory practices, and pharmaceutical quality control. By accurately detecting low levels of genotoxic impurities like piperidine, this method supports the development of safer drug formulations and underscores the importance of stringent quality control measures in the pharmaceutical industry.
PubMed: 38758156
DOI: No ID Found -
Genome Biology and Evolution May 2024The coppery titi monkey (Plecturocebus cupreus) is an emerging non-human primate model system for behavioral and neurobiological research. At the same time, the almost...
The coppery titi monkey (Plecturocebus cupreus) is an emerging non-human primate model system for behavioral and neurobiological research. At the same time, the almost entire absence of genomic resources for the species has hampered insights into the genetic underpinnings of the phenotypic traits of interest. To facilitate future genotype-to-phenotype studies, we here present a high-quality, fully annotated de novo genome assembly for the species with chromosome-length scaffolds spanning the autosomes and chromosome X (scaffold N50 = 130.8 Mb), constructed using data obtained from several orthologous short- and long-read sequencing and scaffolding techniques. With a base-level accuracy of ∼99.99% in chromosome-length scaffolds as well as benchmarking universal single-copy ortholog and k-mer completeness scores of > 99.0% and 95.1% at the genome-level, this assembly represents one of the most complete Pitheciidae genomes to date, making it an invaluable resource for comparative evolutionary genomics research to improve our understanding of lineage-specific changes underlying adaptive traits as well as deleterious mutations associated with disease.
PubMed: 38758096
DOI: 10.1093/gbe/evae108 -
Bioinformatics and Biology Insights 2024Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is...
Gait analysis serves as a critical diagnostic tool for identifying neurologic and musculoskeletal damage. Traditional manual analysis of motion data, however, is labor-intensive and heavily reliant on the expertise and judgment of the therapist. This study introduces a binary classification method for the quantitative assessment of gait impairments, specifically focusing on Duchenne muscular dystrophy (DMD), a prevalent and fatal neuromuscular genetic disorder. The research compares spatiotemporal and sagittal kinematic gait features derived from 2D and 3D human pose estimation trajectories against concurrently recorded 3D motion capture (MoCap) data from healthy children. The proposed model leverages a novel benchmark dataset, collected from YouTube and publicly available datasets of their typically developed peers, to extract time-distance variables (e.g. speed, step length, stride time, and cadence) and sagittal joint angles of the lower extremity (e.g. hip, knee, and knee flexion angles). Machine learning and deep learning techniques are employed to discern patterns that can identify children exhibiting DMD gait disturbances. While the current model is capable of distinguishing between healthy subjects and those with DMD, it does not specifically differentiate between DMD patients and patients with other gait impairments. Experimental results validate the efficacy of our cost-effective method, which relies on recorded RGB video, in detecting gait abnormalities, achieving a prediction accuracy of 96.2% for Support Vector Machine (SVM) and 97% for the deep network.
PubMed: 38757143
DOI: 10.1177/11779322241231108 -
Frontiers in Psychiatry 2024Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to...
BACKGROUND
Psychotherapists need effective tools to monitor changes in the patient's affective perception of the therapist and the therapeutic relationship during sessions to tailor therapeutic interventions and improve treatment outcomes. This study aims to evaluate the factor structure, reliability, and validity of the (SPARQ), a concise self-report measure designed for practical application in real-world psychotherapy settings.
METHODS
Validation data was gathered from ( = 700) adult patients in individual psychotherapy. These patients completed the SPARQ in conjunction with additional measures capturing sociodemographic details, characteristics of therapeutic interventions, individual personality traits, mental health symptom severity, elements of the therapeutic relationship, and session outcomes. This comprehensive approach was employed to assess the construct and criterion-related validity of the SPARQ.
RESULTS
The SPARQ has a two-factor structure: Positive Affect ( = 4, total = .87) and Negative Affect ( = 4, total = .75). Bifactor confirmatory factor analysis (CFA) yielded the following fit indices: [] = 2.53, CFI = .99; TLI = .98; RMSEA = .05; and SRMR = .02. Multi-group CFAs demonstrated measurement invariance (i) across patients who attended psychotherapy sessions in person in remote mode, and (ii) across patients with and without psychiatric diagnoses confirmed metric invariance. Furthermore, the SPARQ showed meaningful correlations with concurrently administered measures.
DISCUSSION
The SPARQ proves to be a valuable instrument in clinical, training, and research contexts, adept at capturing patients' session-level affective responses towards their therapist and perceptions of the therapeutic alliance. Comprehensive descriptive statistics and a range of score precision indices have been reported, intended to serve as benchmarks for future research.
PubMed: 38757138
DOI: 10.3389/fpsyt.2024.1346760 -
Incidence of thrombocytopenia-associated cerebral venous sinus thrombosis: a population-based study.BMJ Neurology Open 2024The identification of SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia (VITT) followed the recognition of a hitherto uncommon clinical syndrome frequently...
OBJECTIVES
The identification of SARS-CoV-2 vaccine-induced immune thrombotic thrombocytopenia (VITT) followed the recognition of a hitherto uncommon clinical syndrome frequently associated with cerebral venous sinus thrombosis (CVST), termed 'thrombosis with thrombocytopenia' syndrome (TTS). While anecdotally recognised as rare, the background incidence of TTS is unknown. We therefore aimed to investigate the background incidence of CVST with TTS in a large, well-defined population-based CVST cohort.
METHODS
We performed an analysis of our previously obtained retrospective population-based cohort of patients with CVST from Adelaide, Australia (2005-2011, comprising an adult population of 953 390) to identify the background incidence of CVST associated with TTS.
RESULTS
Among 105 people with CVST, the background population-based incidence of TTS-associated CVST was 1.2 per million per year (95% CI 0.5 to 2.4). A single case of a severe CVST VITT-like syndrome with multiorgan thrombosis was identified, occurring 3 weeks postrotavirus infection.
CONCLUSIONS
In our population-based study, the background incidence of CVST with associated TTS was very low, and the sole clinically severe case with multiorgan thrombosis occurred following a rotaviral precipitant. Our study establishes a benchmark against which to measure future potential 'TTS' clusters and suggests that viruses other than adenovirus may trigger this syndrome.
PubMed: 38757112
DOI: 10.1136/bmjno-2023-000605 -
Heliyon May 2024In pursuing the goals of sustainable development and transiting from fossil fuel-dependent electricity generation to renewable and sustainable alternatives as endorsed...
In pursuing the goals of sustainable development and transiting from fossil fuel-dependent electricity generation to renewable and sustainable alternatives as endorsed by COP28, Malaysia set a 31 % target for renewable-energy in the power generation mix by 2025. This underlines Malaysia's commitment to combat climate change, mainly by reducing its economy-wide GDP carbon intensity by 45 % from the 2005 levels by 2030. To better understand the effects of renewable energy expansion on the economy, environment, electricity output and input-mix, a computable general equilibrium model is applied using an updated benchmark. The simulation results show that increasing the share of coal and gas in the power generation mix compromises emission reduction targets. Further, there is a trade-off between subsidized natural gas supplies and power generation and exports. The results also show that a larger proportion of renewable energy leads to improved welfare. As the share of gas and coal in renewable energy generation is not very high, its impact on carbon emissions is limited. However, if renewable energy expansion is complemented by subsidy rationalizations, the positive impacts are more pronounced. In terms of policy implications, the findings suggest that Malaysia must step up its emission reduction efforts by augmenting the generation of renewable rather than non-renewable resources. Complementary initiatives such as emission abatement policies and consumption subsidies for refined oil products and fossil-fuel power generation should be rationalized to expand renewable resources, improve energy security, and attain emission reductions.
PubMed: 38756591
DOI: 10.1016/j.heliyon.2024.e30157 -
Health Informatics Journal 2024Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to...
Application of Convolutional neural network in spectrum of Medical image analysis are providing benchmark outputs which converges the interest of many researchers to explore it in depth. Latest preprocessing technique Real ESRGAN (Enhanced super resolution generative adversarial network) and GFPGAN (Generative facial prior GAN) are proving their efficacy in providing high resolution dataset. Optimizer plays a vital role in upgrading the functioning of CNN model. Different optimizers like Gradient descent, Stochastic Gradient descent, Adagrad, Adadelta and Adam etc. are used for classification and segmentation of Medical image but they suffer from slow processing due to their large memory requirement. Stochastic Gradient descent suffers from high variance and is computationally expensive. Dead neuron problem also proves to detrimental to the performance of most of the optimizers. A new optimization technique Gradient Centralization is providing the unparalleled result in terms of generalization and execution time. Our paper explores the next factor which is the employment of new optimization technique, Gradient centralization (GC) to our integrated framework (Model with advanced preprocessing technique). Integrated Framework of Real ESRGAN and GFPGAN with Gradient centralization provides an optimal solution for deep learning models in terms of Execution time and Loss factor improvement.
Topics: Deep Learning; Humans; Neural Networks, Computer; Image Processing, Computer-Assisted; Diagnostic Imaging; Algorithms
PubMed: 38755759
DOI: 10.1177/14604582241255584 -
Trials May 2024Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular...
BACKGROUND
Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches.
METHODS
This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m (≥ 23 kg/m for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA.
DISCUSSION
Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings.
TRIAL REGISTRATION
ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.
Topics: Humans; Diabetes Mellitus, Type 2; Prediabetic State; Artificial Intelligence; Mentoring; Randomized Controlled Trials as Topic; Multicenter Studies as Topic; Treatment Outcome; Risk Reduction Behavior; Time Factors; Adult; Male; Female; Middle Aged; Mobile Applications
PubMed: 38755706
DOI: 10.1186/s13063-024-08177-8 -
Communications Biology May 2024Exposure to pollutants is a potentially crucial but overlooked driver of population declines in shorebirds along the East Asian-Australasian Flyway. We combined...
Exposure to pollutants is a potentially crucial but overlooked driver of population declines in shorebirds along the East Asian-Australasian Flyway. We combined knowledge of moult strategy and life history with a standardised sampling protocol to assess mercury (Hg) contamination in 984 individuals across 33 migratory shorebird species on an intercontinental scale. Over one-third of the samples exceeded toxicity benchmarks. Feather Hg was best explained by moulting region, while habitat preference (coastal obligate vs. non-coastal obligate), the proportion of invertebrates in the diet and foraging stratum (foraging mostly on the surface vs. at depth) also contributed, but were less pronounced. Feather Hg was substantially higher in South China (Mai Po and Leizhou), Australia and the Yellow Sea than in temperate and Arctic breeding ranges. Non-coastal obligate species (Tringa genus) frequently encountered in freshwater habitats were at the highest risk. It is important to continue and expand biomonitoring research to assess how other pollutants might impact shorebirds.
Topics: Animals; Mercury; Animal Migration; Birds; Environmental Monitoring; Australia; Water Pollutants, Chemical; Feathers; Ecosystem; Environmental Pollutants; Charadriiformes; China; East Asian People
PubMed: 38755288
DOI: 10.1038/s42003-024-06254-x -
Scientific Reports May 2024Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually,...
Selecting and isolating various cell types is a critical procedure in many applications, including immune therapy, regenerative medicine, and cancer research. Usually, these selection processes involve some labeling or another invasive step potentially affecting cellular functionality or damaging the cell. In the current proof of principle study, we first introduce an optical biosensor-based method capable of classification between healthy and numerous cancerous cell types in a label-free setup. We present high classification accuracy based on the monitored single-cell adhesion kinetic signals. We developed a high-throughput data processing pipeline to build a benchmark database of ~ 4500 single-cell adhesion measurements of a normal preosteoblast (MC3T3-E1) and various cancer (HeLa, LCLC-103H, MDA-MB-231, MCF-7) cell types. Several datasets were used with different cell-type selections to test the performance of deep learning-based classification models, reaching above 70-80% depending on the classification task. Beyond testing these models, we aimed to draw interpretable biological insights from their results; thus, we applied a deep neural network visualization method (grad-CAM) to reveal the basis on which these complex models made their decisions. Our proof-of-concept work demonstrated the success of a deep neural network using merely label-free adhesion kinetic data to classify single mammalian cells into different cell types. We propose our method for label-free single-cell profiling and in vitro cancer research involving adhesion. The employed label-free measurement is noninvasive and does not affect cellular functionality. Therefore, it could also be adapted for applications where the selected cells need further processing, such as immune therapy and regenerative medicine.
Topics: Cell Adhesion; Humans; Single-Cell Analysis; Kinetics; Mice; Animals; Biosensing Techniques; Cell Line, Tumor
PubMed: 38755203
DOI: 10.1038/s41598-024-61257-2