Deep Eutectic Solvent-Assisted Corrosion Boosting Bulk FeCoNiCrMo High-Entropy Alloys as Highly Efficient Oxygen Evolution Reaction Catalyst.

The key to enhancing water electrolysis efficiency lies in selecting highly efficient catalysts. Currently, high-entropy alloys (HEAs) are utilized in electrocatalysis applications owing to their diverse elemental composition, disordered elemental distribution, and the high solubility of each element, endowing them with excellent catalytic performance. The experiments were conducted using isoatomic FeNiCrMo HEA as a precursor, with a high-activity three-dimensional nanoporous structure rapidly synthesized via electrochemical one-step dealloying in a choline chloride-thiourea (ChCl-TU) deep eutectic solvent (DES). The results indicate that the dealloyed Fe20Co20Ni20Cr20Mo20 HEA mainly consists of two phases: face-centered cubic and σ phases. The imbalance in the distribution of elements in these two phases leads to quite different corrosion speeds with the FCC phase being preferentially corroded. Furthermore, synergistic electron coupling between surface atoms in the three-dimensional nanoporous structure strengthens the behavior of the oxygen evolution reaction (OER). At a current density of 40 mA cm-2, the overpotential after dealloying decreased to 370 mV, demonstrating excellent stability. The technique demonstrated in this work provides a novel approach to improve the catalytic activity of OER.

A machine learning case study to predict rare clinical event of interest: imbalanced data, interpretability, and practical considerations.

Accurate prediction of a rare and clinically important event following study treatment has been crucial in drug development. For instance, the rarity of an adverse event is often commensurate with the seriousness of medical consequences, and delayed detection of the rare adverse event can pose significant or even life-threatening health risks to patients. In this machine learning case study, we demonstrate with an example originated from a real clinical trial setting how to define and solve the rare clinical event prediction problem using machine learning in pharmaceutical industry. The unique contributions of this work include the proposal of a six-step investigation framework that facilitates the communication with non-technical stakeholders and the interpretation of the model performance in terms of practical consequences in the context of patient screenings for conducting a future clinical trial. In terms of machine learning methodology, for data splitting into the training and test sets, we adapt the rare-event stratified split approach (from scikit-learn) to further account for group splitting for multiple records of a patient simultaneously. To handle imbalanced data due to rare events in model training, the cost-sensitive learning approach is employed to give more weights to the minor class and the metrics precision together with recall are used to capture prediction performance instead of the raw accuracy rate. Finally, we demonstrate how to apply the state-of-the-art SHAP values to identify important risk factors to improve model interpretability.

Fully integrated additive manufacturing of an obturator prosthesis for an edentulous patient with a maxillectomy defect.

Maxillary defects pose challenges for prosthodontists, especially when patients have no remaining teeth. This clinical report describes rehabilitation with a complete denture obturator fabricated in 2 visits for an edentulous patient after a maxillectomy. The obturator base and artificial teeth were digitally designed and merged into a 1-piece prosthesis. Following a virtual reduction, the integrated prosthesis and a gingival veneer were calculated and then printed and bonded together to complete the fabrication. Balanced occlusion was achieved with the assistance of a digital occlusion analyzer at the insertion visit. This approach avoided base-tooth assembly deviations and provided a prosthesis with good patient-reported outcomes at the 6-month follow-up.

Differentially Expressed Proteins in the Serum of Elderly Patients Who Experienced Perioperative Neurocognitive Disorders Following Transurethral Resection of the Prostate.

OBJECTIVE: Perioperative neurocognitive disorders (PND) are a group of prevalent neurological complications that often occur in elderly individuals following major or emergency surgical procedures. The etiologies are not fully understood. This study endeavored to investigate novel targets and prediction methods for the occurrence of PND. METHODS: A total of 229 elderly patients diagnosed with prostatic hyperplasia who underwent transurethral resection of the prostate (TURP) combined with spinal cord and epidural analgesia were included in this study. The patients were divided into two groups, the PND group and non-PND group, based on the Z-score method. According to the principle of maintaining consistency between preoperative and intraoperative conditions, three patients from each group were randomly chosen for serum sample collection. isobaric tags for relative and absolute quantification (iTRAQ) proteomics technology was employed to analyze and identify the proteins that exhibited differential expression in the serum samples from the two groups. Bioinformatics analysis was performed on the proteins that exhibited differential expression. RESULTS: Among the 1101 serum proteins analyzed in the PND and non-PND groups, eight differentially expressed proteins were identified in PND patients. Of these, six proteins showed up-regulation, while two proteins showed down-regulation. Further bioinformatics analysis of the proteins that exhibited differential expression revealed their predominant involvement in cellular biological processes, cellular component formation, as well as endocytosis and phagocytosis Additionally, these proteins were found to possess the RING domain of E3 ubiquitin ligase. CONCLUSION: The iTRAQ proteomics technique was employed to analyze the variation in protein expression in serum samples from patients with PND and those without PND. This study successfully identified eight proteins that exhibited differential expression levels between the two groups. Bioinformatics analysis indicates that proteins exhibiting differential expression are primarily implicated in the biological processes associated with microtubules. Investigating the microtubule formation process as it relates to neuroplasticity and synaptic formation may offer valuable insights for enhancing our comprehension and potential prevention of PND. CLINICAL TRIAL REGISTRATION: Registered (ChiCTR2000028836). Date (20190306).

Anthropogenic-driven changes in concentrations and sources of winter volatile organic compounds in an urban environment in the Yangtze River Delta of China between 2013 and 2021.

Volatile organic compounds (VOCs) serve as crucial precursors to surface ozone and secondary organic aerosols (SOA). In response to severe air pollution challenges, China has implemented key air quality control policies from 2013 to 2021. Despite these efforts, a comprehensive understanding of the chemical composition and sources of urban atmospheric VOCs and their responses to emission reduction measures remains limited. Our study focuses on analyzing VOCs composition and concentrations during the winters of 2013 and 2021 through online field observations in urban Nanjing, a typical city in the Yangtze River Delta region of China. Using a machine learning approach, we found a notable reduction in total VOCs concentration from 52.4 ± 30.4 ppb to 33.9 ± 21.6 ppb between the two years, with dominant contributions (approximately 94.3 %) associated with anthropogenic emission control. Furthermore, alkanes emerged as the major contributors (48.6 %) to such anthropogenic-driven decline. The total SOA formation potential decreased by approximately 27.4 %, with aromatics identified as the major contributing species. Positive matrix factorization analysis identified six sources. In 2013, prominent contributors were solid fuel combustion (43.6 %), vehicle emission (16.7 %), and paint and solvent use (12.8 %). By 2021, major sources shifted to solid fuel combustion (31.9 %), liquefied petroleum gas and natural gas (26.8 %), and vehicle emission (25.5 %). Solid fuel combustion emerged as the primary driver for total VOCs reduction. The lifetime carcinogenic risk in 2021 decreased by 72.6 % relative to 2013, emphasizing the need to address liquefied petroleum gas and natural gas source, and vehicle emissions for improved human health. Our findings contribute critical insights for policymakers working on effective air quality management.

Comparison of the efficacy and safety of MAKO robot-assisted total knee arthroplasty versus conventional manual total knee arthroplasty in uncomplicated unilateral total knee arthroplasty a single-centre retrospective analysis.

PURPOSE: To compare the efficacy and safety of MAKO robot-assisted total knee arthroplasty (MA-TKA) with conventional manual total knee arthroplasty (CM-TKA) in patients with end-stage knee osteoarthritis (KOA) during the early postoperative period. METHOD: A retrospective analysis was conducted on 22 patients with KOA who underwent MA-TKA and 26 patients who underwent CM-TKA from April 2023 to July 2023. Hip-knee-ankle angle (HKA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), American Knee Society Score (AKSS), Forgotten Joint Score-12 (FJS-12), visual analogue scale (VAS), and postoperative complications were recorded and compared between the two groups. RESULT: Both groups successfully completed the surgeries. In terms of radiographic parameters, postoperative one month LDFA and HKA in the MA-TKA group were significantly lower than those in the CM-TKA group (P < 0.05). At the one month follow-up, 19 patients (86.4%) in the MA-TKA group had an HKA less than 3°, compared to 20 patients (76.9%) in the CM-TKA group. Clinically, VAS scores at 24 h, 48 h, and 72 h postoperatively were lower in the MA-TKA group both at rest and during activity. At one month and three months postoperatively, AKSS Function Scores and FJS-12 scores in the MA-TKA group were significantly higher than those in the CM-TKA group (P < 0.05). Regarding postoperative complications, no complications occurred in the MA-TKA group, while one patient in the CM-TKA group experienced postoperative knee stiffness, which resolved after physical therapy, with no statistically significant difference (P > 0.05). CONCLUSION: Compared with conventional manual total knee arthroplasty, MAKO robot-assisted TKA demonstrates better short-term clinical efficacy, achieves better alignment planning, and maintains good safety.

Loss-of-function mutation in DDX53 associated with hereditary spastic paraplegia-like disorder.

DEAD-box helicase 53 (DDX53) is a member of the DEAD-box protein family of RNA helicases. Unlike other family members that are responsible for RNA metabolism, the biological function of DDX53 and its impact on the human condition are unclear. Herein, we found a full-length DDX53 deletion mutation in a hereditary spastic paraplegia-like (HSP-like) patient with lower extremity spasticity, walking disorder, visual impairment, and lateral ventricular white matter lesions. Bioinformatic analysis revealed that DDX53 was mainly expressed in the cerebellar cortex and may function as a tissue-specific RNA helicase. Transcriptome analysis showed that the expression of multiple brain-associated genes involved in synapse organization, neuron function, and neuromuscular junctions was affected by DDX53 depletion. Moreover, RNA immunoprecipitation sequencing (RIP-seq) analysis showed that DDX53 interacted with 176 genes, and 96 of these genes were associated with the execution of neurofunction, particularly in the regulation of cell projection organization and nervous system development. Collectively, although a more specified cell or animal model is required to fully understand the functional role of DDX53 in the human brain, we report for the first time that the patient with DDX53 defects exhibits HSP-like symptoms and that DDX53 is essential for maintaining neuronal function, with loss-of-function mutation in DDX53 potentially leading to HSP due to impaired RNA metabolism in the nervous system. KEY MESSAGES: DDX53 deficiency was first reported to be associated with HSP disorder. DDX53 exhibited minimal impact on mitochondrial function. DDX53 impaired RNA metabolism in the nervous system.

Current therapy in amyotrophic lateral sclerosis (ALS): A review on past and future therapeutic strategies.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects the first and second motoneurons (MNs), associated with muscle weakness, paralysis and finally death. The exact etiology of the disease still remains unclear. Currently, efforts to develop novel ALS treatments which target specific pathomechanisms are being studied. The mechanisms of ALS pathogenesis involve multiple factors, such as protein aggregation, glutamate excitotoxicity, oxidative stress, mitochondrial dysfunction, apoptosis, inflammation etc. Unfortunately, to date, there are only two FDA-approved drugs for ALS, riluzole and edavarone, without curative treatment for ALS. Herein, we give an overview of the many pathways and review the recent discovery and preclinical characterization of neuroprotective compounds. Meanwhile, drug combination and other therapeutic approaches are also reviewed. In the last part, we analyze the reasons of clinical failure and propose perspective on the treatment of ALS in the future.

Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids.

The human blood-brain barrier (hBBB) is a highly specialized structure that regulates passage across blood and central nervous system (CNS) compartments. Despite its critical physiological role, there are no reliable in vitro models that can mimic hBBB development and function. Here, we constructed hBBB assembloids from brain and blood vessel organoids derived from human pluripotent stem cells. We validated the acquisition of blood-brain barrier (BBB)-specific molecular, cellular, transcriptomic, and functional characteristics and uncovered an extensive neuro-vascular crosstalk with a spatial pattern within hBBB assembloids. When we used patient-derived hBBB assembloids to model cerebral cavernous malformations (CCMs), we found that these assembloids recapitulated the cavernoma anatomy and BBB breakdown observed in patients. Upon comparison of phenotypes and transcriptome between patient-derived hBBB assembloids and primary human cavernoma tissues, we uncovered CCM-related molecular and cellular alterations. Taken together, we report hBBB assembloids that mimic the core properties of the hBBB and identify a potentially underlying cause of CCMs.

Transcutaneous vagus nerve stimulation improves Long COVID symptoms in a female cohort: a pilot study.

Background: Long COVID, also known as Post-COVID-19 syndrome, is characterized by multisystemic symptoms that persists for weeks to years beyond acute infection. It disproportionately affects women and those with pre-existing anxiety/depression, conditions more prevalent in females. The vagus nerve, with its extensive innervation and regulation of critical bodily functions, has become a focal point for therapeutic interventions. Transcutaneous vagus nerve stimulation (t-VNS) has emerged as a promising non-invasive treatment for COVID-19 conditions. Methods: This pilot study assessed the efficacy of t-VNS in 24 female Long COVID patients (45.8 ± 11.7 years old; 20.2 ± 7.1 months since infection), who underwent a 10-day t-VNS intervention at home (30 min/session, twice a day). Cognition was considered the primary outcome, with anxiety, depression, sleep, fatigue, and smell as secondary outcomes. Outcomes were measured at baseline, post-intervention, and 1-month follow-up. Results: Significant improvements were observed in various cognitive functions, anxiety, depression, and sleep at post-intervention, with benefits remaining or progressing at 1-month follow-up. Improvements in fatigue were delayed, reaching statistical significance at 1-month follow-up compared to baseline. No significant changes were noted in olfactory performance. Conclusion: This pilot study provides preliminary evidence supporting the potential of t-VNS as a therapeutic intervention for female Long COVID patients. The encouraging results justify further rigorous investigation through larger, randomized controlled trials to confirm the efficacy of t-VNS, assess its generalizability to male cohorts, and explore biological markers to inform personalized treatment approaches. Our findings support the allocation of resources to conduct such trials and advance the understanding of t-VNS as a potential treatment for Long COVID.

A simplified digital workflow for the rapid design and fabrication of interim fixed prostheses using an open-access software program.

A digital workflow for the rapid design and fabrication of interim fixed prostheses using an open-access software program and 3-dimensional printing technology is described. After obtaining intraoral scanning data, the prostheses are designed by offset, margin sculpting, and a Boolean operation. Then, the prostheses are finalized and manufactured additively. The use of the open-access software program and simplified design steps enhances the manufacturing efficiency and accessibility of computer-aided design and computer-aided manufacturing of interim restorations.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. METHODS: Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. RESULTS: For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. CONCLUSION: This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Hidden diversity and potential ecological function of phosphorus acquisition genes in widespread terrestrial bacteriophages.

Phosphorus (P) limitation of ecosystem processes is widespread in terrestrial habitats. While a few auxiliary metabolic genes (AMGs) in bacteriophages from aquatic habitats are reported to have the potential to enhance P-acquisition ability of their hosts, little is known about the diversity and potential ecological function of P-acquisition genes encoded by terrestrial bacteriophages. Here, we analyze 333 soil metagenomes from five terrestrial habitat types across China and identify 75 viral operational taxonomic units (vOTUs) that encode 105 P-acquisition AMGs. These AMGs span 17 distinct functional genes involved in four primary processes of microbial P-acquisition. Among them, over 60% (11/17) have not been reported previously. We experimentally verify in-vitro enzymatic activities of two pyrophosphatases and one alkaline phosphatase encoded by P-acquisition vOTUs. Thirty-six percent of the 75 P-acquisition vOTUs are detectable in a published global topsoil metagenome dataset. Further analyses reveal that, under certain circumstances, the identified P-acquisition AMGs have a greater influence on soil P availability and are more dominant in soil metatranscriptomes than their corresponding bacterial genes. Overall, our results reinforce the necessity of incorporating viral contributions into biogeochemical P cycling.

A digital workflow for designing and manufacturing metal frameworks and removable partial dentures: A novel dental technique.

The purpose of this technical report is to demonstrate a fully digital workflow for designing and fabricating metal frameworks and removable partial dentures. After obtaining a digital cast of the dental arch with bilateral distal extension defect, computer-aided design software and 3D printing technology are used for the design and fabrication of the removable partial denture frameworks, denture teeth, and denture bases, instead of the traditional workflow. The assembly of the three components is facilitated through a meticulously structured framework. The technology, which prints metal frameworks, denture bases, and denture teeth through different processes with different materials, achieves full 3D printing technology for making removable partial dentures.

Establishment of Human Pituitary Neuroendocrine Tumor Derived Organoid and Its Pilot Application for Drug Screening.

CONTEXT: Precision medicine for pituitary neuroendocrine tumors (PitNETs) is limited by the lack of reliable research models. OBJECTIVE: To generate patient-derived organoids (PDOs), which could serve as a platform for personalized drug screening for PitNET patients. DESIGN: From July 2019 to May 2022, a total of 32 human PitNET specimens were collected for the establishment of organoids with an optimized culture protocol. SETTING: This study was conducted at Sun Yat-Sen University Cancer Center. PATIENTS: PitNET patients who were pathologically confirmed were enrolled in this study. INTERVENTIONS: Histological staining and whole-exome sequencing were utilized to confirm the pathologic and genomic features of PDOs. A drug response assay on PDOs was also performed. MAIN OUTCOME MEASURES: PDOs retained key genetic and morphological features of their parental tumors. RESULTS: PDOs were successfully established from various types of PitNET samples with an overall success rate of 87.5%. Clinical nonfunctioning PitNETs-derived organoids (22/23, 95.7%) showed a higher likelihood of successful generation compared to those from functioning PitNETs (6/9, 66.7%). Preservation of cellular structure, subtype-specific neuroendocrine profiles, mutational features, and tumor microenvironment heterogeneity from parental tumors was observed. A distinctive response profile in drug tests was observed among the organoids from patients with different subtypes of PitNETs. With the validation of key characteristics from parental tumors in histological, genomic, and microenvironment heterogeneity consistency assays, we demonstrated the predictive value of the PDOs in testing individual drugs. CONCLUSION: The established PDOs, retaining typical features of parental tumors, indicate a translational significance in innovating personalized treatment for refractory PitNETs.

Formoterol Acting via ß2-Adrenoreceptor Restores Mitochondrial Dysfunction Caused by Parkinson's Disease-Related UQCRC1 Mutation and Improves Mitochondrial Homeostasis Including Dynamic and Transport.

Formoterol, a ß2-adrenergic receptor (ß2AR) agonist, shows promise in various diseases, but its effectiveness in Parkinson's disease (PD) is debated, with unclear regulation of mitochondrial homeostasis. This study employed a cell model featuring mitochondrial ubiquinol-cytochrome c reductase core protein 1 (UQCRC1) variants associated with familial parkinsonism, demonstrating mitochondrial dysfunction and dynamic imbalance, exploring the therapeutic effects and underlying mechanisms of formoterol. Results revealed that 24-h formoterol treatment enhanced cell proliferation, viability, and neuroprotection against oxidative stress. Mitochondrial function, encompassing DNA copy number, repatriation, and complex III-linked respiration, was comprehensively restored, along with the dynamic rebalance of fusion/fission events. Formoterol reduced extensive hypertubulation, in contrast to mitophagy, by significantly upregulating protein Drp-1, in contrast to fusion protein Mfn2, mitophagy-related protein Parkin. The upstream mechanism involved the restoration of ERK signaling and the inhibition of Akt overactivity, contingent on the activation of ß2-adrenergic receptors. Formoterol additionally aided in segregating healthy mitochondria for distribution and transport, therefore normalizing mitochondrial arrangement in mutant cells. This study provides preliminary evidence that formoterol offers neuroprotection, acting as a mitochondrial dynamic balance regulator, making it a promising therapeutic candidate for PD.

Deep Learning-Based Psoriasis Assessment: Harnessing Clinical Trial Imaging for Accurate Psoriasis Area Severity Index Prediction.

Introduction: Image-based machine learning holds great promise for facilitating clinical care; however, the datasets often used for model training differ from the interventional clinical trial-based findings frequently used to inform treatment guidelines. Here, we draw on longitudinal imaging of psoriasis patients undergoing treatment in the Ultima 2 clinical trial (NCT02684357), including 2,700 body images with psoriasis area severity index (PASI) annotations by uniformly trained dermatologists. Methods: An image-processing workflow integrating clinical photos of multiple body regions into one model pipeline was developed, which we refer to as the "One-Step PASI" framework due to its simultaneous body detection, lesion detection, and lesion severity classification. Group-stratified cross-validation was performed with 145 deep convolutional neural network models combined in an ensemble learning architecture. Results: The highest-performing model demonstrated a mean absolute error of 3.3, Lin's concordance correlation coefficient of 0.86, and Pearson correlation coefficient of 0.90 across a wide range of PASI scores comprising disease classifications of clear skin, mild, and moderate-to-severe disease. Within-person, time-series analysis of model performance demonstrated that PASI predictions closely tracked the trajectory of physician scores from severe to clear skin without systematically over- or underestimating PASI scores or percent changes from baseline. Conclusion: This study demonstrates the potential of image processing and deep learning to translate otherwise inaccessible clinical trial data into accurate, extensible machine learning models to assess therapeutic efficacy.