Metabolic reprogramming: a new option for the treatment of spinal cord injury.

Spinal cord injuries impose a notably economic burden on society, mainly because of the severe after-effects they cause. Despite the ongoing development of various therapies for spinal cord injuries, their effectiveness remains unsatisfactory. However, a deeper understanding of metabolism has opened up a new therapeutic opportunity in the form of metabolic reprogramming. In this review, we explore the metabolic changes that occur during spinal cord injuries, their consequences, and the therapeutic tools available for metabolic reprogramming. Normal spinal cord metabolism is characterized by independent cellular metabolism and intercellular metabolic coupling. However, spinal cord injury results in metabolic disorders that include disturbances in glucose metabolism, lipid metabolism, and mitochondrial dysfunction. These metabolic disturbances lead to corresponding pathological changes, including the failure of axonal regeneration, the accumulation of scarring, and the activation of microglia. To rescue spinal cord injury at the metabolic level, potential metabolic reprogramming approaches have emerged, including replenishing metabolic substrates, reconstituting metabolic couplings, and targeting mitochondrial therapies to alter cell fate. The available evidence suggests that metabolic reprogramming holds great promise as a next-generation approach for the treatment of spinal cord injury. To further advance the metabolic treatment of the spinal cord injury, future efforts should focus on a deeper understanding of neurometabolism, the development of more advanced metabolomics technologies, and the design of highly effective metabolic interventions.

Adrenal Volume Quantitative Visualization Tool by Multiple Parameters and an nnU-Net Deep Learning Automatic Segmentation Model.

Abnormalities in adrenal gland size may be associated with various diseases. Monitoring the volume of adrenal gland can provide a quantitative imaging indicator for such conditions as adrenal hyperplasia, adrenal adenoma, and adrenal cortical adenocarcinoma. However, current adrenal gland segmentation models have notable limitations in sample selection and imaging parameters, particularly the need for more training on low-dose imaging parameters, which limits the generalization ability of the models, restricting their widespread application in routine clinical practice. We developed a fully automated adrenal gland volume quantification and visualization tool based on the no new U-Net (nnU-Net) for the automatic segmentation of deep learning models to address these issues. We established this tool by using a large dataset with multiple parameters, machine types, radiation doses, slice thicknesses, scanning modes, phases, and adrenal gland morphologies to achieve high accuracy and broad adaptability. The tool can meet clinical needs such as screening, monitoring, and preoperative visualization assistance for adrenal gland diseases. Experimental results demonstrate that our model achieves an overall dice coefficient of 0.88 on all images and 0.87 on low-dose CT scans. Compared to other deep learning models and nnU-Net model tools, our model exhibits higher accuracy and broader adaptability in adrenal gland segmentation.

Heparin is an effective treatment for preventing liver failure after hepatectomy.

BACKGROUND: Posthepatectomy liver failure (PHLF) is one of the most important causes of death following liver resection. Heparin, an established anticoagulant, can protect liver function through a number of mechanisms, and thus, prevent liver failure. AIM: To look at the safety and efficacy of heparin in preventing hepatic dysfunction after hepatectomy. METHODS: The data was extracted from Multiparameter Intelligent Monitoring in Intensive Care III (MIMIC-III) v1. 4 pinpointed patients who had undergone hepatectomy for liver cancer, subdividing them into two cohorts: Those who were injected with heparin and those who were not. The statistical evaluations used were unpaired t-tests, Mann-Whitney U tests, chi-square tests, and Fisher's exact tests to assess the effect of heparin administration on PHLF, duration of intensive care unit (ICU) stay, need for mechanical ventilation, use of continuous renal replacement therapy (CRRT), incidence of hypoxemia, development of acute kidney injury, and ICU mortality. Logistic regression was utilized to analyze the factors related to PHLF, with propensity score matching (PSM) aiming to balance the preoperative disparities between the two groups. RESULTS: In this study, 1388 patients who underwent liver cancer hepatectomy were analyzed. PSM yielded 213 matched pairs from the heparin-treated and control groups. Initial univariate analyses indicated that heparin potentially reduces the risk of PHLF in both matched and unmatched samples. Further analysis in the matched cohorts confirmed a significant association, with heparin reducing the risk of PHLF (odds ratio: 0.518; 95% confidence interval: 0.295-0.910; P = 0.022). Additionally, heparin treatment correlated with improved short-term postoperative outcomes such as reduced ICU stay durations, diminished requirements for respiratory support and CRRT, and lower incidences of hypoxemia and ICU mortality. CONCLUSION: Liver failure is an important hazard following hepatic surgery. During ICU care heparin administration has been proved to decrease the occurrence of hepatectomy induced liver failure. This indicates that heparin may provide a hopeful option for controlling PHLF.

Prefrontal cortex neurons encode ambient light intensity differentially across regions and layers.

While light can affect emotional and cognitive processes of the medial prefrontal cortex (mPFC), no light-encoding was hitherto identified in this region. Here, extracellular recordings in awake mice revealed that over half of studied mPFC neurons showed photosensitivity, that was diminished by inhibition of intrinsically photosensitive retinal ganglion cells (ipRGCs), or of the upstream thalamic perihabenular nucleus (PHb). In 15% of mPFC photosensitive neurons, firing rate changed monotonically along light-intensity steps and gradients. These light-intensity-encoding neurons comprised four types, two enhancing and two suppressing their firing rate with increased light intensity. Similar types were identified in the PHb, where they exhibited shorter latency and increased sensitivity. Light suppressed prelimbic activity but boosted infralimbic activity, mirroring the regions' contrasting roles in fear-conditioning, drug-seeking, and anxiety. We posit that prefrontal photosensitivity represents a substrate of light-susceptible, mPFC-mediated functions, which could be ultimately studied as a therapeutical target in psychiatric and addiction disorders.

A leak K+ channel TWK-40 sustains the rhythmic motor program.

Leak potassium (K+) currents, conducted by two-pore domain K+ (K2P) channels, are critical for the stabilization of the membrane potential. The effect of K2P channels on motor rhythm remains enigmatic. We show here that the K2P TWK-40 contributes to the rhythmic defecation motor program (DMP) in Caenorhabditis elegans. Disrupting TWK-40 suppresses the expulsion defects of nlp-40 and aex-2 mutants. By contrast, a gain-of-function (gf) mutant of twk-40 significantly reduces the expulsion frequency per DMP cycle. In situ whole-cell patch clamping demonstrates that TWK-40 forms an outward current that hyperpolarize the resting membrane potential of dorsorectal ganglion ventral process B (DVB), an excitatory GABAergic motor neuron that activates expulsion muscle contraction. In addition, TWK-40 substantially contributes to the rhythmic activity of DVB. Specifically, DVB Ca2+ oscillations exhibit obvious defects in loss-of-function (lf) mutant of twk-40. Expression of TWK-40(gf) in DVB recapitulates the expulsion deficiency of the twk-40(gf) mutant, and inhibits DVB Ca2+ oscillations in both wild-type and twk-40(lf) animals. Moreover, DVB innervated enteric muscles also exhibit rhythmic Ca2+ defects in twk-40 mutants. In summary, these findings establish TWK-40 as a crucial neuronal stabilizer of DMP, linking leak K2P channels with rhythmic motor activity.

Liver injury in paraquat poisoning: A retrospective cohort study.

BACKGROUND AND AIMS: Liver injury is one of the common complications of paraquat (PQ) poisoning, but whether the degree of liver injury is related to patient prognosis is still controversial. This study aimed to investigate whether liver injury was a risk factor for death in PQ-poisoned patients. METHODS: We conducted a retrospective cohort study of PQ-poisoned patients from the past 10 years (2011-2020) from a large tertiary academic medical centre in China. PQ-poisoned patients were divided into a normal liver function group (n = 580) and a liver injury group (n = 60). Propensity score matching (PSM) analysis was then performed. RESULTS: A total of 640 patients with PQ poisoning were included in this study. To reduce the impact of bias, dose of PQ, urinary PQ concentration and time from poisoning to hospital admission were matched between the two groups. A 3:1 PSM analysis was performed, ultimately including 240 patients. Compared with the normal liver function group, patients in the liver injury group were older, had a higher R value ([ALT/ULN]/[ALP/ULN]) (p < .001) and had a higher mortality rate. Cox regression analysis showed that there was no significant association between alanine aminotransferase, alkaline phosphatase, total bilirubin levels and hazard of death, but age, PQ dose, creatine kinase isoenzyme, creatine kinase, white blood cell count, neutrophil percentage and lymphocyte percentage were associated with mortality in patients with PQ poisoning. CONCLUSIONS: The occurrence of liver injury within 48 h after PQ poisoning was a risk factor for mortality, and such liver injury was likely of a hepatocellular nature. Age, PQ dose, creatine kinase isoenzyme and white blood cell count were positively correlated with mortality, while creatine kinase, percentage of neutrophils and lymphocytes were inversely correlated.

Cell signaling and transcriptional regulation of osteoblast lineage commitment, differentiation, bone formation, and homeostasis.

The initiation of osteogenesis primarily occurs as mesenchymal stem cells undergo differentiation into osteoblasts. This differentiation process plays a crucial role in bone formation and homeostasis and is regulated by two intricate processes: cell signal transduction and transcriptional gene expression. Various essential cell signaling pathways, including Wnt, BMP, TGF-ß, Hedgehog, PTH, FGF, Ephrin, Notch, Hippo, and Piezo1/2, play a critical role in facilitating osteoblast differentiation, bone formation, and bone homeostasis. Key transcriptional factors in this differentiation process include Runx2, Cbfß, Runx1, Osterix, ATF4, SATB2, and TAZ/YAP. Furthermore, a diverse array of epigenetic factors also plays critical roles in osteoblast differentiation, bone formation, and homeostasis at the transcriptional level. This review provides an overview of the latest developments and current comprehension concerning the pathways of cell signaling, regulation of hormones, and transcriptional regulation of genes involved in the commitment and differentiation of osteoblast lineage, as well as in bone formation and maintenance of homeostasis. The paper also reviews epigenetic regulation of osteoblast differentiation via mechanisms, such as histone and DNA modifications. Additionally, we summarize the latest developments in osteoblast biology spurred by recent advancements in various modern technologies and bioinformatics. By synthesizing these insights into a comprehensive understanding of osteoblast differentiation, this review provides further clarification of the mechanisms underlying osteoblast lineage commitment, differentiation, and bone formation, and highlights potential new therapeutic applications for the treatment of bone diseases.

From disruption to adaptation: Response of phytoplankton communities in representative impounded lakes to China's South-to-North Water Diversion Project.

Impounded lakes are often interconnected in large-scale water diversion projects to form a coordinated system for water allocation and regulation. The alternating runoff and transferred water can significantly impact local ecosystems, which are initially reflected in the sensitive phytoplankton. Nonetheless, limited information is available on the temporal dynamics and assembly patterns of phytoplankton community in impounded lakes responding to continuous and periodic water diversion. Herein, a long-term monitoring from 2013 to 2020 were conducted to systematically investigate the response of phytoplankton community, including its characteristics, stability, and the ecological processes governing community assembly, in representative impounded lakes to the South-to-North Water Diversion Project (SNWDP) in China. In the initial stage of the SNWDP, the phytoplankton diversity indices experienced a decrease during both non-water diversion periods (8.5 %∼21.2 %) and water diversion periods (5.6 %∼12.2 %), implying a disruption in the aquatic ecosystem. But the regular delivery of high-quality water from the Yangtze River gradually increased phytoplankton diversity and mediated ecological assembly processes shifting from stochastic to deterministic. Meanwhile, reduced nutrients restricted the growth of phytoplankton, pushing species to interact more closely to maintain the functionality and stability of the co-occurrence network. The partial least squares path model revealed that ecological process (path coefficient = 0.525, p < 0.01) and interspecies interactions in networks (path coefficient = -0.806, p < 0.01) jointly influenced the keystone and dominant species, ultimately resulting in an improvement in stability (path coefficient = 0.878, p < 0.01). Overall, the phytoplankton communities experienced an evolutionary process from short-term disruption to long-term adaptation, demonstrating resilience and adaptability in response to the challenges posed by the SNWDP. This study revealed the response and adaptation mechanism of phytoplankton communities in impounded lakes to water diversion projects, which is helpful for maintaining the lake ecological health and formulating rational water management strategies.

Anti-tumor therapy through high ROS performance induced by Ag nanoenzyme from boron cluster with halloysite clay nanotubes.

The conventional silver nanoparticles (Ag NPs) are characterized with high loading rate and stacking phenomenon, leading to shedding caused biotoxicity and low catalytic efficiency. This seriously hinders their application in biomedicine. Here, we modified the highly dispersible Ag NPs and Ag single-atoms (SAs) synthesis by combining the halloysite clay nanotubes (HNTs) and dodecahydro-dodecaborate (closo-[B12H12]2-) to increase the biocompatible properties and decrease the loading rate. This novel Ag single-atom nanoenzyme alongside Ag NPs nanoenzyme avoid the elevated-temperature calcination while maintaining the exceptionally high-level efficiency of Ag utilization via the reducibility and coordination stabilization of closo-[B12H12]2- and HNTs. With theoretical calculation and electron paramagnetic resonance, we confirmed that both Ag SAzymes and Ag NPs in HNT@B12H12@Ag nanoenzyme are capable decompose the H2O2 into hydroxyl radical (·OH). For the application, we investigated the catalytic activity in the tumor cells and antitumor effects of HNT@B12H12@Ag nanoenzyme both in vitro and in vivo, and confirmed that it effectively suppressed melanoma growth through ·OH generation, with limited biotoxicity. This study provides a novel Ag nanoenzyme synthesis approach to increase the possibility of its clinical application.

A wearable non-enzymatic sensor for continuous monitoring of glucose in human sweat.

To enhance personalized diabetes management, there is a critical need for non-invasive wearable electrochemical sensors made from flexible materials to enable continuous monitoring of sweat glucose levels. The main challenge lies in developing glucose sensors with superior electrochemical characteristics and high adaptability. Herein, we present a wearable sensor for non-enzymatic electrochemical glucose analysis. The sensor was synthesized using hydrothermal and one-pot preparation methods, incorporating gold nanoparticles (AuNPs) functionalized onto aminated multi-walled carbon nanotubes (AMWCNTs) as an efficient catalyst, and crosslinked with carboxylated styrene butadiene rubber (XSBR) and PEDOT:PSS. The sensors were then integrated onto screen-printed electrodes (SPEs) to create flexible glucose sensors (XSBR-PEDOT:PSS-AMWCNTs/AuNPs/SPE). Operating under neutral conditions, the sensor exhibits a linear range of 50 µmol/L to 600 µmol/L, with a limit of detection limit of 3.2 µmol/L (S/N = 3), enabling the detection of minute glucose concentrations. The flexible glucose sensor maintains functionality after 500 repetitions of bending at a 180° angle, without significant degradation in performance. Furthermore, the sensor exhibits exceptional stability, repeatability, and resistance to interference. Importantly, we successfully monitored changes in sweat glucose levels by applying screen-printed electrodes to human skin, with results consistent with normal physiological blood glucose fluctuations. This study details the fabrication of a wearable sensor characterized by ease of manufacture, remarkable flexibility, high sensitivity, and adaptability for non-invasive blood glucose monitoring through non-enzymatic electrochemical analysis. Thus, this streamlined fabrication process presents a novel approach for non-invasive, real-time blood glucose level monitoring.

An innovative triple interface reinforced photocatalytic system based on BiOCl/BaTiO3@Co-BDC-MOF composite for the simultaneous detoxification of Cr(VI) and sulfamethoxazole.

The development of effective photocatalysts for the reduction of Cr(VI) and the degradation of antibiotics remains a challenge. The present work reports the development of a novel heterojunction composite material, BiOCl/BaTiO3@Co-BDC-MOF (BOC/BTO@Co-MOF), based on solvothermal techniques. To characterize the surface and bulk features of the material, techniques such as FE-SEM, HR-TEM, BET/BJH, XPS, FT-IR, p-XRD, and UV-Vis-DRS were used. Based on the results, the BiOCl/BaTiO3 nanocomposites are uniformly dispersed on the rod-shaped Co-BDC MOF, resulting in a layered texture on the surface. A further advantage of the composite structure is the strong interfacial enhancement facilitating the separation of photoexcited electron-hole pairs. Also, compared to its pristine counterparts, the heterostructure material exhibited excellent surface area and pore properties. The photocatalytic efficiency towards reduction and degradation of Cr(VI)/SMX pollutants were evaluated by optimizing various analytical parameters, such as pH, catalytic loading concentrations, analyte concentration, and scavenger role. The specially designed BOC/BTO@Co-MOF composite achieved a 96.5% Cr(VI) reduction and 98.2% SMX degradation under 60.0-90.0 min of visible light illumination at pH 3.0. This material is highly reusable and has a six-time recycling potential. The findings of this study contribute to a better understanding of the efficient decontamination of inorganic and organic pollutants in water purification systems.

Artificial Intelligence Outcome Prediction in Neonates with Encephalopathy (AI-OPiNE).

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning algorithm to predict 2-year neurodevelopmental outcomes in neonates with hypoxic-ischemic encephalopathy (HIE) using MRI and basic clinical data. Materials and Methods In this study, MRI data of term neonates with encephalopathy in the High Dose Erythropoietin for Asphyxia (HEAL) trial (ClinicalTrials.gov: NCT02811263), who were enrolled from 17 institutions between January 25th, 2017 and October ninth, 2019, were retrospectively analyzed. The harmonized MRI protocol included T1-weighted, T2-weighted, and diffusion tensor imaging. Deep learning classifiers were trained to predict the primary outcome of the HEAL trial (death or any neurodevelopmental impairment [NDI] at 2 years) using multisequence MRI and basic clinical variables, including sex and gestational age at birth. Model performance was evaluated on a test sets comprising 10% of cases from 15 institutions (in-distribution test set, n = 41) and 100% of cases from 2 institutions (out-of-distribution test set, n = 41). Model performance in predicting additional secondary outcomes, including death alone, was also assessed. Results For the 414 neonates (mean gestational age, 39 weeks ± 1.4, 232 males, 182 females), in the study cohort, 198 (48%) died or had any NDI at 2 years. The deep learning model achieved an area under the receiver operating characteristic curve (AUC) of 0.74 (95% CI: 0.60-0.86) and 63% accuracy on the in-distribution test set and an AUC of 0.77 (95% CI: 0.63-0.90) and 78% accuracy on the out-of-distribution test set. Performance was similar or better for predicting secondary outcomes. Conclusion Deep learning analysis of neonatal brain MRI yielded high performance for predicting 2-year neurodevelopmental outcomes. ©RSNA, 2024.

Prevalence of subthreshold depression and its related factors in Chinese college students: A cross-sectional study.

Objective: To investigate the prevalence of subthreshold depression among Chinese college students and to explore the related factors. Methods: The research subjects were Chinese college students participating in the "2022 Psychology and Behavior Investigation of Chinese Residents (PBICR-2022)". Data on respondents' general characteristics, quality of life, perceived pressure, family communication, perceived social support, self-efficacy, and depression status were gathered. To investigate the association between each variable and the risk of subthreshold depression, statistical analyses, including chi-square tests and rank sum tests were conducted. Furthermore, a binary stepwise logistic regression was employed to establish the regression model of the factors related to subthreshold depression among Chinese college students. Results: A prevalence of subthreshold depression of about 39.7 % was found among the 8934 respondents. Logistic regression analysis revealed that respondents who are female, have chronic diseases, are in debt, experience significant impacts from epidemic control policies, have lower self-assessed quality of life, experience challenges in family communication, perceive lower social support, have lower self-efficacy, and feel higher perceived pressure are more likely to develop subthreshold depression compared to the control group. (P < 0.05). Conclusion: The prevalence rate of subthreshold depression among Chinese college students was found to be approximately 40 %. Female college students suffering from chronic diseases, with households in debt, greatly impacted by epidemic control policies, and experiencing high perceived stress, may be at risk for subthreshold depression among Chinese college students. On the other hand, strong family communication, perceived social support, and self-efficacy were identified as potential protective factors. In order to facilitate timely screening, diagnosis, and treatment of subthreshold depression in Chinese college students, it is crucial for the government, local communities, colleges, and families to prioritize the mental health of college students and implement targeted measures accordingly.

Arterial Spin-Labeling MR Imaging in the Detection of Intracranial Arteriovenous Malformations in Patients with Hereditary Hemorrhagic Telangiectasia.

BACKGROUND AND PURPOSE: Hereditary hemorrhagic telangiectasia (HHT) is an autosomal dominant disease that causes vascular malformations in a variety of organs and tissues, including brain AVMs. Because brain AVMs have the potential to cause disabling or fatal intracranial hemorrhage, detection of these lesions before rupture is the goal of screening MR imaging/MRA examinations in patients with HHT. Prior studies have demonstrated superior sensitivity for HHT-related brain AVMs by using postcontrast MR imaging sequences as compared with MRA alone. We now present data regarding the incremental benefit of including arterial spin-labeling (ASL) perfusion sequences as part of MR imaging/MRA screening in patients with this condition. MATERIALS AND METHODS: We retrospectively analyzed 831 patients at the UCSF Hereditary Hemorrhagic Telangiectasia Center of Excellence. Of these, 42 patients had complete MR imaging/MRA, ASL perfusion scans, and criterion-standard DSA data. Two neuroradiologists reviewed imaging studies and a third provided adjudication when needed. RESULTS: Eight patients had no brain AVMs detected on DSA. The remaining 34 patients had 57 brain AVMs on DSA. Of the 57 identified AVMs, 51 (89.5%) were detected on ASL and 43 (75.4%) were detected on conventional MR imaging/MRA sequences (P = .049), with 8 lesions detected on ASL perfusion but not on conventional MR imaging. CONCLUSIONS: ASL provides increased sensitivity for brain AVMs in patients with HHT. Inclusion of ASL should be considered as part of comprehensive MR imaging/MRA screening protocols for institutions taking care of patients with HHT.

Contribution of inwardly rectifying potassium channel 4.1 in orofacial neuropathic pain: Regulation of pannexin 3 via the reactive oxygen species-activated P38 MAPK signal pathway.

The involvement of inwardly rectifying potassium channel 4.1 (Kir4.1) in neuropathic pain has been established. However, there is limited understanding of the downstream mechanism through which Kir4.1 contributes to orofacial neuropathic pain. The objective of this study was to examine the regulation of Kir4.1 on the expression of pannexin 3 (Panx3) in the trigeminal ganglion (TG) and the underlying mechanism in the context of orofacial neuropathic pain caused by chronic constriction injury of the infraorbital nerve (CCI-ION). The study observed a significant increase in Panx3 expression in the TG of mice with CCI-ION. Inhibition of Panx3 in the TG of CCI-ION mice resulted in alleviation of orofacial mechanical allodynia. Furthermore, conditional knockdown (CKD) of Kir4.1 in the TG of both male and female mice led to mechanical allodynia and upregulation of Panx3 expression. Conversely, overexpression of Kir4.1 decreased Panx3 levels in the TG and relieved mechanical allodynia in CCI-ION mice. In addition, silencing Kir4.1 in satellite glial cells (SGCs) decreased Panx3 expression and increased the phosphorylation of P38 MAPK. Moreover, silencing Kir4.1 in SGCs increased the levels of reactive oxygen species (ROS). The elevated phosphorylation of P38 MAPK resulting from Kir4.1 silencing was inhibited by using a superoxide scavenger known as the tempol. Silencing Panx3 in the TG in vivo attenuated the mechanical allodynia caused by Kir4.1 CKD. In conclusion, these findings suggest that the reduction of Kir4.1 promotes the expression of Panx3 by activating the ROS-P38 MAPK signalling pathway, thus contributing to the development of orofacial neuropathic pain.

Predictions of Colloidal Molecular Aggregation Using AI/ML Models.

To facilitate the triage of hits from small molecule screens, we have used various AI/ML techniques and experimentally observed data sets to build models aimed at predicting colloidal aggregation of small organic molecules in aqueous solution. We have found that Naïve Bayesian and deep neural networks outperform logistic regression, recursive partitioning tree, support vector machine, and random forest techniques by having the lowest balanced error rate (BER) for the test set. Derived predictive classification models consistently and successfully discriminated aggregator molecules from nonaggregator hits. An analysis of molecular descriptors in favor of colloidal aggregation confirms previous observations (hydrophobicity, molecular weight, and solubility) in addition to undescribed molecular descriptors such as the fraction of sp3 carbon atoms (Fsp3), and electrotopological state of hydroxyl groups (ES_Sum_sOH). Naïve Bayesian modeling and scaffold tree analysis have revealed chemical features/scaffolds contributing the most to colloidal aggregation and nonaggregation, respectively. These results highlight the importance of scaffolds with high Fsp3 values in promoting nonaggregation. Matched molecular pair analysis (MMPA) has also deciphered context-dependent substitutions, which can be used to design nonaggregator molecules. We found that most matched molecular pairs have a neutral effect on aggregation propensity. We have prospectively applied our predictive models to assist in chemical library triage for optimal plate selection diversity and purchase for high throughput screening (HTS) in drug discovery projects.

Narrative review of stereotactic body radiation therapy combined with tyrosine kinase inhibitors for oligometastatic EGFR-mutated non-small cell lung cancer: present and future developments.

Background and Objective: A significant number of individuals diagnosed with non-small cell lung cancer (NSCLC) have distant metastases, and the concept of oligometastatic NSCLC has shown promise in achieving a cure. Stereotactic body radiation therapy (SBRT) is currently considered a viable treatment option for a limited number of tumor metastases. It has also been demonstrated that third-generation tyrosine kinase inhibitors (TKIs) are effective in extending the survival of patients with epidermal growth factor receptor (EGFR)-mutated NSCLC. Hence, the combination of SBRT with third-generation TKIs holds the potential to enhance treatment efficacy in patients with oligometastatic EGFR-mutated NSCLC. This review aimed to assess the possibility of combining SBRT with TKIs as an optimum treatment option for patients with oligometastatic EGFR-mutated NSCLC. Methods: We performed a narrative review by searching the PubMed, Web of Science, Elsevier and ClinicalTrials.gov databases for articles published in the English language from January 2009 to February 2024 and by reviewing the bibliographies of key references to identify important literature related to combining SBRT with third-generation TKIs in oligometastatic EGFR-mutated NSCLC. Key Content and Findings: This review aimed to assess the viability of combining SBRT and EGFR-TKIs in oligometastatic EGFR-mutated NSCLC. Current clinical trials suggest that the combined therapies have better progression free survival (PFS) when using SBRT as either concurrent with EGFR-TKIs or consolidated with EGFR-TKIs. Furthermore, research with third-generation EGFR-TKIs and SBRT combinations has demonstrated tolerable toxicity levels without significant additional adverse effects as compared to prior therapies. However, further clinical trials are required to establish its effectiveness. Conclusions: The combined approach of SBRT and TKIs can effectively impede the progression of oligometastatic NSCLC in patients harboring EGFR mutations and, most notably, can prolong progression-free survival rates. However, the feasibility of combining SBRT with third-generation TKIs in clinical trials remains unclear.

Assessing the prognostic utility of clinical and radiomic features for COVID-19 patients admitted to ICU: challenges and lessons learned.

Severe cases of COVID-19 often necessitate escalation to the Intensive Care Unit (ICU), where patients may face grave outcomes, including mortality. Chest X-rays play a crucial role in the diagnostic process for evaluating COVID-19 patients. Our collaborative efforts with Michigan Medicine in monitoring patient outcomes within the ICU have motivated us to investigate the potential advantages of incorporating clinical information and chest X-ray images for predicting patient outcomes. We propose an analytical workflow to address challenges such as the absence of standardized approaches for image pre-processing and data utilization. We then propose an ensemble learning approach designed to maximize the information derived from multiple prediction algorithms. This entails optimizing the weights within the ensemble and considering the common variability present in individual risk scores. Our simulations demonstrate the superior performance of this weighted ensemble averaging approach across various scenarios. We apply this refined ensemble methodology to analyze post-ICU COVID-19 mortality, an occurrence observed in 21% of COVID-19 patients admitted to the ICU at Michigan Medicine. Our findings reveal substantial performance improvement when incorporating imaging data compared to models trained solely on clinical risk factors. Furthermore, the addition of radiomic features yields even larger enhancements, particularly among older and more medically compromised patients. These results may carry implications for enhancing patient outcomes in similar clinical contexts.