Assessing the Role of Locus Coeruleus Degeneration in Essential Tremor and Parkinson's Disease with Sleep Disorders.

Background: Previous studies have demonstrated the importance of the locus coeruleus (LC) in sleep-wake regulation. Both essential tremor (ET) and Parkinson's disease (PD) share common sleep disorders, such as poor quality of sleep (QoS). LC pathology is a feature of both diseases. A question arises regarding the contribution of LC degeneration to the occurrence of poor QoS. Objective: To evaluate the association between LC impairment and sleep disorders in ET and PD patients. Methods: A total of 83 patients with ET, 124 with PD, and 83 healthy individuals were recruited and divided into ET/PD with/without poor QoS (Sle/NorET and Sle/NorPD) subgroups according to individual Pittsburgh Sleep Quality Index (PSQI) score. Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) and free-water imaging derived from diffusion MRI were performed. Subsequently, we evaluated the association between contrast-to-noise ratio of LC (CNRLC) and free-water value of LC (FWLC) with PSQI scores in ET and PD groups. Results: CNRLC was significantly lower in ET (p = 0.047) and PD (p = 0.018) than in healthy individuals, whereas no significant difference was found in FWLC among the groups. No significant differences were observed in CNR/FWLC between patients with/without sleep disorders after multiple comparison correction. No correlation was identified between CNR/FWLC and PSQI in ET and PD patients. Conclusions: LC degeneration was observed in both ET and PD patients, implicating its involvement in the pathophysiology of both diseases. Additionally, no significant association was observed between LC integrity and PSQI, suggesting that LC impairment might not directly relate to overall QoS.

Combined Use of Emapalumab With Ruxolitinib and Dexamethasone as an Effective Treatment for Epstein-Barr Virus-Associated Hemophagocytic Lymphohistiocytosis Complicated With Multiorgan Damage and Severe Infection.

Anti-interferon-γ monoclonal antibody emapalumab and JAK1/2 inhibitors ruxolitinib have been widely reported for the treatment of hemophagocytic lymphohistiocytosis (HLH) recently. These targeted drugs have fewer side effects and may provide new options for patients with HLH who are refractory to previous treatment or intolerant to chemotherapy. Herein, we reported a case of Epstein-Barr virus-related HLH, which did not respond well to HLH-94 plus ruxolitinib and developed severe fungal infection. The disease was successfully controlled after a combination therapy of emapalumab, ruxolitinib, and dexamethasone.

Microglial CMPK2 promotes neuroinflammation and brain injury after ischemic stroke.

Neuroinflammation plays a significant role in ischemic injury, which can be promoted by oxidized mitochondrial DNA (Ox-mtDNA). Cytidine/uridine monophosphate kinase 2 (CMPK2) regulates mtDNA replication, but its role in neuroinflammation and ischemic injury remains unknown. Here, we report that CMPK2 expression is upregulated in monocytes/macrophages and microglia post-stroke in humans and mice, respectively. Microglia/macrophage CMPK2 knockdown using the Cre recombination-dependent adeno-associated virus suppresses the inflammatory responses in the brain, reduces infarcts, and improves neurological outcomes in ischemic CX3CR1Cre/ERT2 mice. Mechanistically, CMPK2 knockdown limits newly synthesized mtDNA and Ox-mtDNA formation and subsequently blocks NLRP3 inflammasome activation in microglia/macrophages. Nordihydroguaiaretic acid (NDGA), as a CMPK2 inhibitor, is discovered to reduce neuroinflammation and ischemic injury in mice and prevent the inflammatory responses in primary human monocytes from ischemic patients. Thus, these findings identify CMPK2 as a promising therapeutic target for ischemic stroke and other brain disorders associated with neuroinflammation.

Estimating global annual gross primary production based on satellite-derived phenology and maximal carbon uptake capacity.

The high uncertainty regarding global gross primary production (GPP) remains unresolved. This study explored the relationships between phenology, physiology, and annual GPP to provide viable alternatives for accurate estimation. A statistical model of integrated phenology and physiology (SMIPP) was developed using GPP data from 145 FLUXNET sites to estimate the annual GPP for various vegetation types. By employing the SMIPP model driven by satellite-derived datasets of the global carbon uptake period (CUP) and maximal carbon uptake capacity (GPPmax), the global annual GPP was estimated for the period from 2001-2018. The results demonstrated that the SMIPP model accurately predicted annual GPP, with relative root mean square error values ranging from 11.20‒19.29% for forest types and 20.49‒35.71% for non-forest types. However, wetlands, shrublands, and evergreen forests exhibited relatively low accuracies. The average, trend, and interannual variation of global GPP during 2001-2018 were 132.6 Pg C yr-1, 0.25 Pg C yr-2, and 1.57 Pg C yr-1, respectively. They were within the ranges estimated in other global GPP products. Sensitivity analysis revealed that GPPmax had comparable effects to CUP in high-latitude regions but significantly greater impacts at the global scale, with sensitivity coefficients of 0.85 ± 0.23 for GPPmax and 0.46 ± 0.28 for CUP. This study provides a simple and practical method for estimating global annual GPP and highlights the influence of GPPmax and CUP on global-scale annual GPP.

Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph.

Accurately predicting molecular properties is a challenging but essential task in drug discovery. Recently, many mono-modal deep learning methods have been successfully applied to molecular property prediction. However, mono-modal learning is inherently limited as it relies solely on a single modality of molecular representation, which restricts a comprehensive understanding of drug molecules. To overcome the limitations, we propose a multimodal fused deep learning (MMFDL) model to leverage information from different molecular representations. Specifically, we construct a triple-modal learning model by employing Transformer-Encoder, Bidirectional Gated Recurrent Unit (BiGRU), and graph convolutional network (GCN) to process three modalities of information from chemical language and molecular graph: SMILES-encoded vectors, ECFP fingerprints, and molecular graphs, respectively. We evaluate the proposed triple-modal model using five fusion approaches on six molecule datasets, including Delaney, Llinas2020, Lipophilicity, SAMPL, BACE, and pKa from DataWarrior. The results show that the MMFDL model achieves the highest Pearson coefficients, and stable distribution of Pearson coefficients in the random splitting test, outperforming mono-modal models in accuracy and reliability. Furthermore, we validate the generalization ability of our model in the prediction of binding constants for protein-ligand complex molecules, and assess the resilience capability against noise. Through analysis of feature distributions in chemical space and the assigned contribution of each modal model, we demonstrate that the MMFDL model shows the ability to acquire complementary information by using proper models and suitable fusion approaches. By leveraging diverse sources of bioinformatics information, multimodal deep learning models hold the potential for successful drug discovery.

Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

Does livestock-Manure-Derived Biochar Suitable for the Stabilization of Cadmium and Zinc in Contaminated Soil?

Both livestock-manure and livestock-manure-derived biochar have been used to remediate heavy metal-contaminated soil. However, direct comparisons of the heavy metal stabilization efficiency of livestock-manure and EQC-manure-biochar (derived from an equal quantity of corresponding livestock-manure) are limited. In the present study, the effect of livestock-manures and EQC-manure-biochars on soil properties and heavy metal bioavailability and leachability were compared using two contrasting soils (Ferralsols and Fluvisols). The results showed that both the livestock-manures and EQC-manure-biochars significantly changed soil pH, available phosphorus, available potassium, alkaline nitrogen and organic matter content (p < 0.05), but the trends were variable. In Ferralsols, the DTPA-extractable Cd and Zn decreased by -0.38%~5.70% and - 3.79%~9.98% with livestock-manure application and by -7.99%~7.23% and - 5.67%~7.17% with EQC-manure-biochars application. In Fluvisols, the DTPA-extractable Cd and Zn decreased by 13.39%~17.41% and - 45.26%~14.24% with livestock-manure application and by 10.76%~16.90% and - 36.38%~16.37% with EQC-manure-biochar application. Furthermore, the change in TCLP-extractable Cd and Zn in both soils was similar to that of DTPA-extractable Cd and Zn. Notably, the Cd and Zn stabilization efficiency of the EQC-manure-biochars was no better than that of the corresponding livestock-manures. These results suggest that the use of livestock-manure-derived biochar is not cost-effective for the remediation of heavy metal-contaminated soil.

Low interleukin-10 level indicates a good prognosis in Salmonella enterica serovar typhimurium-induced pediatric hemophagocytic lymphohistiocytosis: A case report.

BACKGROUND: Secondary hemophagocytic lymphohistiocytosis (sHLH) triggered by Salmonella enterica serovar Typhimurium is rare in pediatric patients. There is no consensus on how to treat S. typhimurium-triggered sHLH. CASE SUMMARY: A 9-year-old boy with intermittent fever for 3 d presented to our hospital with positive results for S. typhimurium, human rhinovirus, and Mycoplasma pneumoniae infections. At the time of admission to our institution, the patient's T helper 1/T helper 2 cytokine levels were 326 pg/mL for interleukin 6 (IL-6), 9.1 pg/mL for IL-10, and 246.7 pg/mL for interferon-gamma (IFN-γ), for which the ratio of IL-10 to IFN-γ was 0.04. In this study, the patient received meropenem, linezolid, and cefoperazone/sulbactam in combination with high-dose methylprednisolone therapy (10 mg/kg/d for 3 d) and antishock supportive treatment twice. After careful evaluation, this patient did not receive HLH chemotherapy and recovered well. CONCLUSION: S. Typhimurium infection-triggered sHLH patient had a ratio of IL-10 to IFN-γ ≤ 1.33, an IL-10 concentration ≤ 10.0 pg/mL, and/or an IFN-γ concentration ≤ 225 pg/mL at admission. Early antimicrobial and supportive treatment was sufficient, and the HLH-94/2004 protocol was not necessary under these conditions.

Generation and characterization of the iPS cell line (SYSUSHi001-A) derived from the peripheral blood mononuclear cells (PBMCs) of a 33-year-old patient with acute myeloid leukemia (AML).

We successfully developed an induced pluripotent stem cell (iPSC) line, SYSUSHi001-A, from the peripheral blood mononuclear cells (PBMC) of a patient with Acute Myeloid Leukemia, harboring two genetic mutations (XPO1: c.591-4_591-3dupTT; PALB2: c.3296C > T; p.T1099M). This iPSC line was facilitated through the use of episomal plasmids encoding OCT4, SOX2, KLF4, L-MYC, and human miR-302. The SYSUSHi001-A iPSC line exhibited characteristic embryonic stem cell-like morphology, maintained the XPO1 and PALB2 mutations, expressed key pluripotency markers, preserved a normal karyotype (46, XY), and demonstrated the ability to differentiate into cells from all three germ layers in vitro.

Measurable residual disease monitoring by ddPCR in the early posttransplant period complements the traditional MFC method to predict relapse after HSCT in AML/MDS: a multicenter retrospective study.

BACKGROUND: Droplet digital PCR (ddPCR) is widely applied to monitor measurable residual disease (MRD). However, there are limited studies on the feasibility of ddPCR-MRD monitoring after allogeneic hematopoietic stem cell transplantation (allo-HSCT), especially targeting multiple molecular markers simultaneously. METHODS: Our study collected samples from patients with acute myeloid leukemia (AML) or high-risk myelodysplastic syndrome (MDS) in complete remission after allo-HSCT between January 2018 and August 2021 to evaluate whether posttransplant ddPCR-MRD monitoring can identify patients at high risk of relapse. RESULTS: Of 152 patients, 58 (38.2%) were MRD positive by ddPCR within 4 months posttransplant, with a median variant allele frequency of 0.198%. The detectable DTA mutations (DNMT3A, TET2, and ASXL1 mutations) after allo-HSCT were not associated with an increased risk of relapse. After excluding DTA mutations, patients with ddPCR-MRD positivity had a significantly higher cumulative incidence of relapse (CIR, 38.7% vs. 9.7%, P < 0.001) and lower rates of relapse-free survival (RFS, 55.5% vs. 83.7%, P < 0.001) and overall survival (OS, 60.5% vs. 90.5%, P < 0.001). In multivariate analysis, ddPCR-MRD positivity of non-DTA genes was an independent adverse predictor for CIR (hazard ratio [HR], 4.02; P < 0.001), RFS (HR, 2.92; P = 0.002) and OS (HR, 3.12; P = 0.007). Moreover, the combination of ddPCR with multiparameter flow cytometry (MFC) can further accurately identify patients at high risk of relapse (F+/M+, HR, 22.44; P < 0.001, F+/M-, HR, 12.46; P < 0.001 and F-/M+, HR, 4.51; P = 0.003). CONCLUSION: ddPCR-MRD is a feasible approach to predict relapse after allo-HSCT in AML/MDS patients with non-DTA genes and is more accurate when combined with MFC. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06000306. Registered 17 August 2023 -Retrospectively registered ( https://clinicaltrials.gov/study/NCT06000306?term=NCT06000306&rank=1 ).

Opportunities, challenges, and future directions of large language models, including ChatGPT in medical education: a systematic scoping review

BACKGROUND: ChatGPT is a large language model (LLM) based on artificial intelligence (AI) capable of responding in multiple languages and generating nuanced and highly complex responses. While ChatGPT holds promising applications in medical education, its limitations and potential risks cannot be ignored. METHODS: A scoping review was conducted for English articles discussing ChatGPT in the context of medical education published after 2022. A literature search was performed using PubMed/MEDLINE, Embase, and Web of Science databases, and information was extracted from the relevant studies that were ultimately included. RESULTS: ChatGPT exhibits various potential applications in medical education, such as providing personalized learning plans and materials, creating clinical practice simulation scenarios, and assisting in writing articles. However, challenges associated with academic integrity, data accuracy, and potential harm to learning were also highlighted in the literature. The paper emphasizes certain recommendations for using ChatGPT, including the establishment of guidelines. Based on the review, 3 key research areas were proposed: cultivating the ability of medical students to use ChatGPT correctly, integrating ChatGPT into teaching activities and processes, and proposing standards for the use of AI by medical students. CONCLUSION: ChatGPT has the potential to transform medical education, but careful consideration is required for its full integration. To harness the full potential of ChatGPT in medical education, attention should not only be given to the capabilities of AI but also to its impact on students and teachers.

Differential influences of rest tremor on brain fiber architecture in essential tremor and Parkinson's disease.

BACKGROUND: Rest tremor is a movement disorder commonly found in diseases like Parkinson's disease (PD) and essential tremor (ET). Rest tremor typically shows slower progression in PD, but more severe progression in ET. However, the underlying white matter organization of rest tremor behind PD and ET remains unclear. METHODS: This study included 57 ET patients (40 without rest tremor (ETWR), 17 with rest tremor (ETRT)), 68 PD patients (34 without rest tremor (PDWR), 34 with rest tremor (PDRT)), and 62 normal controls (NC). Fixel-based analysis was used to evaluate the structural changes of white matter in rest tremor in these different diseases. RESULTS: The fiber-bundle cross-section (FC) of the right non-decussating dentato-rubro-thalamic tract and several fibers outside the dentato-rubro-thalamic pathway in ETWR were significantly higher than that in NC. The fiber density and cross-section of the left nigro-pallidal in PDWR is significantly lower than that in NC, while the FC of bilateral nigro-pallidal in PDRT is significantly lower than that in NC. CONCLUSION: ET patients with pure action tremor showed over-activation of fiber tracts. However, when superimposed with rest tremor, ET patients no longer exhibited over-activation of fiber tracts, but rather showed a trend of fiber tract damage. Except for the nigro-pallidal degeneration in all PD, PDRT will not experience further deterioration in fiber organization. These results provide important insights into the unique effects of rest tremor on brain fiber architecture in ET and PD.

A chromosome-level genome assembly of East Asia endemic minnow Zacco platypus.

Zacco platypus is an endemic colorful freshwater minnow that is intensively distributed in East Asia. In this study, two adult female individuals collected from Haihe River basin were used for karyotypic study and genome sequencing, respectively. The karyotype formula of Z. platypus is 2N = 48 = 18 M + 24SM/ST + 6 T. We used PacBio long-read sequencing and Hi-C technology to assemble a chromosome-level genome of Z. platypus. As a result, an 814.87 Mb genome was assembled with the PacBio long reads. Subsequently, 98.64% assembled sequences were anchored into 24 chromosomes based on the Hi-C data. The chromosome-level assembly contained 54 scaffolds with a N50 length of 32.32 Mb. Repeat elements accounted for 52.35% in genome, and 24,779 protein-coding genes were predicted, with 92.11% were functionally annotated with the public databases. BUSCO analysis yielded a completeness score of 96.5%. This high-quality genome assembly provides valuable resources for future functional genomic research, comparative genomics, and evolutionary studies of genus Zacco.

Enhanced removal of phosphate from aqueous solutions by oxygen vacancy-rich MgO microspheres: Performance and mechanism.

The efficient removal of phosphate from water environments was extremely significant to control eutrophication of water bodies and prevent further deterioration of water quality. In this study, oxygen vacancy-rich magnesium oxide (OV-MgO) microspheres were synthesized by a simple solvothermal method coupling high-temperature calcination. The effects of adsorbent dosage, contact time, initial pH and coexisting components on phosphate adsorption performance were examined. The physicochemical properties of OV-MgO microspheres and the phosphate removal mechanisms were analyzed by various characterization techniques. The maximum adsorption capacity predicted by the Sips isotherm model was 379.7 mg P/g for OV-MgO microspheres. The phosphate adsorption in this study had a fast adsorption kinetics and a high selectivity. OV-MgO microspheres had a good acid resistance for phosphate adsorption, but their adsorption capacity decreased under alkaline conditions. The electrostatic attraction, ligand exchange, surface precipitation, inner-sphere surface complexation and oxygen vacancy capture were mainly responsible for efficient removal of phosphate from aqueous solutions. This study probably promoted the development of oxygen vacancy-rich metal (hydr)oxides with potential application prospects.

The longitudinal volumetric and shape changes of subcortical nuclei in Parkinson's disease.

Brain structural changes in Parkinson's disease (PD) are progressive throughout the disease course. Changes in surface morphology with disease progression remain unclear. This study aimed to assess the volumetric and shape changes of the subcortical nuclei during disease progression and explore their association with clinical symptoms. Thirty-four patients and 32 healthy controls were enrolled. The global volume and shape of the subcortical nuclei were compared between patients and controls at baseline. The volume and shape changes of the subcortical nuclei were also explored between baseline and 2 years of follow-up. Association analysis was performed between the volume of subcortical structures and clinical symptoms. In patients with PD, there were significantly atrophied areas in the left pallidum and left putamen, while in healthy controls, the right putamen was dilated compared to baseline. The local morphology of the left pallidum was correlated with Mini Mental State Examination scores. The left putamen shape variation was negatively correlated with changes in Unified Parkinson's Disease Rating Scale PART III scores. Local morphological atrophy of the putamen and pallidum is an important pathophysiological change in the development of PD, and is associated with motor symptoms and cognitive status in patients with PD.

Joint effects of heat-humidity compound events on drowning mortality in Southern China.

BACKGROUND: Several previous studies have examined the association of ambient temperature with drowning. However, no study has investigated the effects of heat-humidity compound events on drowning mortality. METHODS: The drowning mortality data and meteorological data during the five hottest months (May to September) were collected from 46 cities in Southern China (2013-2018 in Guangdong, Hunan and Zhejiang provinces). Distributed lag non-linear model was first conducted to examine the association between heat-humidity compound events and drowning mortality at city level. Then, meta-analysis was employed to pool the city-specific exposure-response associations. Finally, we analysed the additive interaction of heat and humidity on drowning mortality. RESULTS: Compared with wet-non-hot days, dry-hot days had greater effects (excess rate (ER)=32.34%, 95% CI: 24.64 to 40.50) on drowning mortality than wet-hot days (ER=14.38%, 95%CI: 6.80 to 22.50). During dry-hot days, males (ER=42.40%, 95% CI: 31.92 to 53.72), adolescents aged 0-14 years (ER=45.00%, 95% CI: 21.98 to 72.35) and urban city (ER=36.91%, 95% CI: 23.87 to 51.32) showed higher drowning mortality risk than their counterparts. For wet-hot days, males, adolescents and urban city had higher ERs than their counterparts. Attributable fraction (AF) of drowning attributed to dry-hot days was 23.83% (95% CI: 21.67 to 26.99) which was significantly higher than that for wet-hot days (11.32%, 95% CI: 9.64 to 13.48%). We also observed that high temperature and low humidity had an additive interaction on drowning mortality. CONCLUSION: We found that dry-hot days had greater drowning mortality risk and burden than wet-hot days, and high temperature and low humidity might have synergy on drowning mortality.

Microstructural alterations of the hypothalamus in Parkinson's disease and probable REM sleep behavior disorder.

BACKGROUND: Whether there is hypothalamic degeneration in Parkinson's disease (PD) and its association with clinical symptoms and pathophysiological changes remains controversial. OBJECTIVES: We aimed to quantify microstructural changes in hypothalamus using a novel deep learning-based tool in patients with PD and those with probable rapid-eye-movement sleep behavior disorder (pRBD). We further assessed whether these microstructural changes associated with clinical symptoms and free thyroxine (FT4) levels. METHODS: This study included 186 PD, 67 pRBD, and 179 healthy controls. Multi-shell diffusion MRI were scanned and mean kurtosis (MK) in hypothalamic subunits were calculated. Participants were assessed using Unified Parkinson's Disease Rating Scale (UPDRS), RBD Questionnaire-Hong Kong (RBDQ-HK), Hamilton Depression Rating Scale (HAMD), and Activity of Daily Living (ADL) Scale. Additionally, a subgroup of PD (n = 31) underwent assessment of FT4. RESULTS: PD showed significant decreases of MK in anterior-superior (a-sHyp), anterior-inferior (a-iHyp), superior tubular (supTub), and inferior tubular hypothalamus when compared with healthy controls. Similarly, pRBD exhibited decreases of MK in a-iHyp and supTub. In PD group, MK in above four subunits were significantly correlated with UPDRS-I, HAMD, and ADL. Moreover, MK in a-iHyp and a-sHyp were significantly correlated with FT4 level. In pRBD group, correlations were observed between MK in a-iHyp and UPDRS-I. CONCLUSIONS: Our study reveals that microstructural changes in the hypothalamus are already significant at the early neurodegenerative stage. These changes are associated with emotional alterations, daily activity levels, and thyroid hormone levels.

Geometric deep learning for the prediction of magnesium-binding sites in RNA structures.

Magnesium ions (Mg2+) are essential for the folding, functional expression, and structural stability of RNA molecules. However, predicting Mg2+-binding sites in RNA molecules based solely on RNA structures is still challenging. The molecular surface, characterized by a continuous shape with geometric and chemical properties, is important for RNA modelling and carries essential information for understanding the interactions between RNAs and Mg2+ ions. Here, we propose an approach named RNA-magnesium ion surface interaction fingerprinting (RMSIF), a geometric deep learning-based conceptual framework to predict magnesium ion binding sites in RNA structures. To evaluate the performance of RMSIF, we systematically enumerated decoy Mg2+ ions across a full-space grid within the range of 2 to 10 Å from the RNA molecule and made predictions accordingly. Visualization techniques were used to validate the prediction results and calculate success rates. Comparative assessments against state-of-the-art methods like MetalionRNA, MgNet, and Metal3DRNA revealed that RMSIF achieved superior success rates and accuracy in predicting Mg2+-binding sites. Additionally, in terms of the spatial distribution of Mg2+ ions within the RNA structures, a majority were situated in the deep grooves, while a minority occupied the shallow grooves. Collectively, the conceptual framework developed in this study holds promise for advancing insights into drug design, RNA co-transcriptional folding, and structure prediction.

Epidemiological features and a survival nomogram for primary lymphoma of the male genital tract.

Primary lymphoma of the male genital tract (PLMGT) is rare, and data on its epidemiology and prognosis are lacking. Our study aimed to estimate the incidence and develop a predictive nomogram for PLMGT. We pooled the incidence and survival data of PLMGT over the last 20 years from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence rates were calculated by year of diagnosis, age, race, and histology. Independent prognostic factors selected by Cox regression analysis were used to develop a nomogram for predicting overall survival (OS). Our study enrolled 1312 patients with PLMGT. The overall incidence rate of PLMGT was 0.437/1,000,000 during 2000-2019. OS was associated with age, marital status, histological subtype, Ann Arbor stage, and therapeutic strategy, which were used to construct nomograms to predict 1-, 3-, and 5-year OS rates. Receiver operating characteristic curves, calibration plots, and decision curve analysis showed good performance of the nomogram. Based on the total score of each patient from the nomogram, the patients were clustered into three risk groups, and the risk stratification model was more successful in predicting clinical outcomes than the traditional Ann Arbor staging system. The incidence rate of PLMGT has remained relatively stable over the past two decades. For the OS of patients with PLMGT, we established a novel predictive nomogram involving all independent risk factors obtained from the SEER database and developed a corresponding risk classification system that showed better predictive performance than the Ann Arbor staging system.

Preparation of Fe-BN-C catalysts derived from ZIF-8 and their performance in the oxygen reduction reaction.

Enhancing the oxygen reduction reaction (ORR) activity and stability of the fuel cell cathode electrocatalysts and reducing their costs are critical. In response to this need, Fe, B, and N co-doped hollow mesoporous carbon materials were prepared by a simple chemical doping one-step pyrolysis method using ZIF-8 as a precursor. The results showed that the optimized catalyst displayed a higher limiting current density (6.154 mA cm-2) and half-wave potential (0.859 V), which showed significant enhancement compared with the Pt/C catalyst (5.487 mA cm-2 and 0.853 V). Moreover, the optimized catalyst had outstanding long-term stability with a current density retention higher than 91% after 36 000 s of stability testing. This work provides a facile strategy for the design of outstanding ORR performance of non-precious metal oxygen reduction catalysts.