Transcription Factor ETV4 Activates AURKA to Promote PD-L1 Expression and Mediate Immune Escape in Lung Adenocarcinoma.

INTRODUCTION: The occurrence and progression of lung adenocarcinoma (LUAD) impair T-cell immune responses, causing immune escape and subsequently affecting the efficacy of immunotherapy in patients. Aurora kinase A (AURKA) is upregulated in varying cancers, but its role in LUAD immune escape is elusive. This work attempted to explore molecular mechanisms of AURKA regulation in LUAD immune escape. METHODS: Through bioinformatics analysis, AURKA level in LUAD was evaluated, and potential upstream transcription factors of AURKA were predicted using hTFtarget. ETS variant transcription factor 4 (ETV4) expression in LUAD was analyzed through The Cancer Genome Atlas. Pearson's correlation analysis was then utilized to test the correlation between AURKA and ETV4. Interaction and binding between AURKA and ETV4 were validated through dual-luciferase assay and chromatin immunoprecipitation. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) tested relative mRNA expression of AURKA and ETV4 in LUAD cells, cell counting kit-8 assayed cell viability, and Western blot analysis was conducted to determine the protein level of programmed death-ligand 1 (PD-L1). Coculture of LUAD cells with activated CD8+ T cells was carried out, and an LDH assay was used to assess the cytotoxicity of CD8+ T cells against LUAD cells. Interferon-γ (IFN-γ), interleukin-2 (IL-2), and tumor necrosis factor-α (TNF-α) levels in the coculture system were assessed by enzyme-linked immunosorbent assay (ELISA). Western blot assessed protein levels of JAK2, p-JAK2, STAT3, and p-STAT3. RESULTS: Compared to normal tissues, AURKA and ETV4 were upregulated in tumor tissues, and AURKA presented a negative association with CD8+ T-cell immune infiltration but a positive association with PD-L1. qRT-PCR unveiled significantly upregulated mRNA of AURKA and ETV4 in LUAD cells compared to normal lung epithelial cells. Knockdown of AURKA significantly decreased cell viability and PD-L1 protein level in LUAD cells, enhanced cytotoxicity of CD8+ T cells against LUAD cells and IFN-γ, IL-2, and TNF-α expression, while overexpression of AURKA yielded opposite results. Furthermore, the knockdown of ETV4 could reverse the oncogenic characteristics of cells caused by AURKA overexpression. CONCLUSION: Our study illustrated that ETV4/AURKA axis promoted PD-L1 expression, suppressed CD8+ T-cell activity, and mediated immune escape in LUAD by regulating the JAK2/STAT3 signaling pathway.

Large Electromechanical Response in a Polycrystalline Alkali-Deficient (K,Na)NbO3 Thin Film on Silicon.

The demand for large electromechanical performance in lead-free polycrystalline piezoelectric thin films is driven by the need for compact, high-performance microelectromechanical systems (MEMS) based devices operating at low voltages. Here we significantly enhance the electromechanical response in a polycrystalline lead-free oxide thin film by utilizing lattice-defect-induced structural inhomogeneities. Unlike prior observations in mismatched epitaxial films with limited low-frequency enhancements, we achieve large electromechanical strain in a polycrystalline (K,Na)NbO3 film integrated on silicon. This is achieved by inducing self-assembled Nb-rich planar faults with a nonstoichiometric composition. The film exhibits an effective piezoelectric coefficient of 565 pm V-1 at 1 kHz, surpassing those of lead-based counterparts. Notably, lattice defect growth is substrate-independent, and the large electromechanical response is extended to even higher frequencies in a polycrystalline film. Improved properties arise from unique lattice defect morphology and frequency-dependent relaxation behavior, offering a new route to remarkable electromechanical response in polycrystalline thin films.

Disseminated cryptococcal infection with pulmonary involvement presenting as diffuse cavitary nodules in an immunocompromised patient: a case report.

BACKGROUND: Disseminated cryptococcal infection is especially prone to occur in immunosuppressed hosts. We herein report the case of an immunosuppressed girl with disseminated cryptococcal infection in whom pulmonary cryptococcosis (PC) presented as diffuse cavitary pulmonary nodules, a finding which has rarely been reported. CASE PRESENTATION: A 16-year-old immunocompromised girl presented with fever and a non-productive cough. A chest computed tomography (CT) scan revealed diffuse pulmonary nodules with cavities. Subsequent results were consistent with disseminated cryptococcosis with Cryptococcus identified in her blood, bone marrow and cerebrospinal fluid cultures. Thus, the patient was diagnosed with disseminated cryptococcal infection with PC, cryptococcus meningitis, cryptococcus osteomyelitis and cryptococcus sepsis. After antifungal treatment, the patient demonstrated both clinical and chest radiological improvement. CONCLUSION: The atypical clinical manifestations of a disseminated cryptococcal infection and the rare manner of chest CT findings of PC reported in our case are easy to misdiagnose. It is necessary to carry out a thorough search for a definitive diagnosis using various methods.

Noninvasive Mechanical Ventilation in Patients with Viral Pneumonia-Associated Acute Respiratory Distress Syndrome: An Observational Retrospective Study.

Objectives: Appropriate mechanical ventilation may change the prognosis of patients with viral pneumonia-associated acute respiratory distress syndrome (ARDS). This study aimed to identify the factors associated with the success of noninvasive ventilation in the management of patients with ARDS secondary to respiratory viral infection. Methods: In this retrospective cohort study, all patients with viral pneumonia-associated ARDS were divided into the noninvasive mechanical ventilation (NIV) success group and the NIV failure group. The demographic and clinical data of all patients were collected. The factors associated with the success of noninvasive ventilation were identified by the logistic regression analysis. Results: Among this cohort, 24 patients with an average age of 57.9 ± 17.0 years received successful NIVs, and NIV failure occurred in 21 patients with an average age of 54.1 ± 14.0 years. The independent influencing factors for the success of the NIV were the acute physiology and chronic health evaluation (APACHE) II score (odds ratio (OR): 1.83, 95% confidence interval (CI): 1.10-3.03) and lactate dehydrogenase (LDH) (OR: 1.011, 95% CI: 1.00-1.02). When the oxygenation index (OI) is <95 mmHg, APACHE II > 19, and LDH > 498 U/L, the sensitivity and specificity of predicting a failed NIV were (66.6% (95% CI: 43.0%-85.4%) and 87.5% (95% CI: 67.6%-97.3%)); (85.7% (95% CI: 63.7%-97.0%) and 79.1% (95% CI: 57.8%-92.9%)); (90.4% (95% CI: 69.6%-98.8%) and 62.5% (95% CI: 40.6%-81.2%)), respectively. The areas under the receiver operating characteristic curve (AUC) of the OI, APACHE II scores, and LDH were 0.85, which was lower than the AUC of the OI combined with LDH and the APACHE II score (OLA) of 0.97 (P=0.0247). Conclusions: Overall, patients with viral pneumonia-associated ARDS receiving successful NIV have lower mortality rates than those for whom NIV failed. In patients with influenza A-associated ARDS, the OI may not be the only indicator of whether NIV can be used; a new indicator of NIV success may be the OLA.

Fast Photostriction in Ferroelectrics.

Light-induced nonthermal strain, known as the photostrictive effect, offers a potential way to excite mechanical strain and acoustic wave remotely. The anisotropic photostrictive effect induced by the combination of bulk photovoltaic effect (BPVE) and converse piezoelectric effect in ferroelectric materials is known as too small and slow for the applications requiring a high strain rate, such as ultrasound generation and high-speed signal transmission. Here, a strategy to achieve high rate dynamic photostrictive strain by utilizing local fast responses under modulating continuous light excitation in the resonance condition is reported. A strain rate of 8.06 × 10-3  s-1 is demonstrated under continuous light excitation, which is at least one order of magnitude higher than previous studies on bulk samples as seen in the literature. The significant photostrictive response exists even in depoled ferroelectric material without overall polarization. The theoretical analyses show that fast ferroelectric photostriction can be obtained through the combinational interaction mechanism of local BPVE and local converse piezoelectric effect existing only in the microscopic scale, thus circumventing the slow and low efficient BPVE charging up process across the macroscopic electrical terminals. The achieved fast photostriction and new understandings will open new opportunities to realize future wireless signal transmission and light-acoustic devices.

A Bi-level representation learning model for medical visual question answering.

Medical Visual Question Answering (VQA) targets at answering questions related to given medical images and it contains tremendous potential in healthcare services. However, researches on medical VQA are still facing challenges, particularly on how to learn a fine-grained multimodal semantic representation from relatively small volume of data resources for answer prediction. Moreover, the long-tailed distribution labels of medical VQA data frequently result in poor performance of models. To this end, we propose a novel bi-level representation learning model with two reasoning modules to learn bi-level representations for the medical VQA task. One is sentence-level reasoning to learn sentence-level semantic representations from multimodal input. The other is token-level reasoning that employs an attention mechanism to generate a multimodal contextual vector by fusing image features and word embeddings. The contextual vector is used to filter irrelevant semantic representations from sentence-level reasoning to generate a fine-grained multimodal representation. Furthermore, a label-distribution-smooth margin loss is proposed to minimize generalization error bound of long-tailed distribution datasets by modifying margin bound of different labels in training set. Based on standard VQA-Rad dataset and PathVQA dataset, the proposed model achieves 0.7605 and 0.5434 on accuracy, 0.7741 and 0.5288 on F1-score, respectively, outperforming a set of state-of-the-art baseline models.

Disease risk analysis for schizophrenia patients by an automatic AHP framework.

BACKGROUND: Based on more than 15 million follow-up records of 404,426 patients from Guangdong Mental Health Center over the past 10 years, this study aims to propose a disease risk analysis and prediction model to support chronic disease management and clinical research for schizophrenia patients. METHODS: Based on a mental health information and intelligent data processing platform, we design an automatic AHP framework called AutoAHP to analyze and predict the disease risks of schizophrenia patients. Through automatic extraction, transformation and integration of follow-up data in the real world such as demography, treatment, and the disease course, a chronic database of patient status is established. In combination with age-period-cohort, logistic regression and Cox models, we apply the AutoAHP to assess disease risk and implement risk prediction in practice. RESULTS: A list of essential factors for risk prediction are identified, including annual changes in mental health policy, public support, regional difference, patient gender, compliance, and social function. After the verification of 1,222,038 complete disease course and treatment records of 256,050 patients, the AutoAHP framework achieves a precision of 0.923, a recall of 0.924, and a F1 of 0.923. The model is demonstrated to be superior to general models and has better performance in risk prediction. CONCLUSIONS: Aiming at the risk assessment of patients with schizophrenia which is influenced by factors, such as time, region and complication, the AutoAHP framework is able to be applied as a model in combination with logistic regression and Cox models to support clinical analysis of disease risk related factors and assist decision-making in chronic disease management.

Pulmonary Co-infection with Cryptococcus Species and Pneumocystis jirovecii in an Old Patient Without a Previous Predisposing Illness.

BACKGROUND: Cryptococcosis and pneumocystosis are opportunistic infections which are more common in immunosuppressed individuals. Herein, we report a rare case of coinfection of pulmonary cryptococcosis (PC) and Pneumocystis jirovecii pneumonia (PJP) in a patient without a previous predisposing illness. Case presentation A 76-year-old man was admitted to our hospital with complaints of cough, expectoration, shortness of breath, and fever. A chest computed tomography (CT) showed multiple nodules with diffuse ground-glass opacities (GGOs) in both lungs. The patient was diagnosed as extrinsic allergic alveolitis (Pigeon breeder's lung). After treatment with corticosteroids, the patient improved with significant absorption of GGOs on chest CT. However, pulmonary nodules gradually enlarged and such lesions could not be explained by EAA. Based on the positivity of serum cryptococcal antigen and pathological examination of lung nodule which confirmed the presence of Cryptococcus spores, PC was diagnosed later and fluconazole was administered. However, repeated chest CT performed about 2 months after antifungal treatment showed significantly increased GGOs in both lungs. The pathological examination of new lung lesions revealed the presence of P. jirovecii. The patient was finally diagnosed having coinfection of PC and PJP and sulfamethoxazole was further prescribed. Thereafter, the patient improved again with significant absorption of GGOs as noted on chest CT. CONCLUSIONS: Concomitant PC and PJP is very rare, especially in a patient without a previous predisposing illness. Additionally, when pulmonary lesions cannot be completely explained by one kind of infectious disease, the possibility of mixed infection should be considered.

The psychosis analysis in real-world on a cohort of large-scale patients with schizophrenia.

BACKGROUND: With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important outcome to monitor. Our study conducted an epidemiological study of psychosis in Guangdong province, with 20 million real-world follow-up records in the last decade. METHODS: Data was collected from Guangdong mental health information platform from 2010 to 2019, which had standardized disease registration and follow-up management for nearly 600,000 patients with six categories of mental diseases and 400,000 patients with schizophrenia. We conducted clinical staging for the disease course of the patients and divided the data with various factors into different stages of disease. Quantitative analysis was utilized to investigate the high relevant indicators to the disease. The results were projected on geography map for regional distribution analysis. RESULTS: The majority cases of mental disease incidence were between the age of 15 and 29, while the peak age for both male and female was between 20 to 24 years old. The disease course with the largest number of patients' cases was between 5 to 10 years. The therapeutic effect of patients gradually decreased with the development of disease course, while the risk increased with the disease course. The analysis of influencing factors showed that poor economic conditions incurred higher risk scores, and good medication adherence was effective in improving treatment outcomes. In addition, receiving good education contributed to the reduction of the risk of schizophrenia and the improvement of the efficiency of early treatment. Through the analysis of regional distribution of schizophrenia disease, developed economic conditions and favorable resource conditions could promote the reduction of disease risk, while in economically backward regions, it often accompanied with lower therapeutic effect and higher disease risk. CONCLUSIONS: Certain demographic factors had a relatively prominent impact on the therapeutic effect and risk of schizophrenia, such as high-quality medication adherence. Therapeutic effect and risk were highly correlated. Backward economic conditions often associated with poor efficacy and higher risk assessment, and the developed economy and better medical resource are beneficial for the treatment of psychotic.

A multicenter RCT of noninvasive ventilation in pneumonia-induced early mild acute respiratory distress syndrome.

RATIONALE: Our pilot study suggested that noninvasive ventilation (NIV) reduced the need for intubation compared with conventional administration of oxygen on patients with "early" stage of mild acute respiratory distress syndrome (ARDS, PaO2/FIO2 between 200 and 300). OBJECTIVES: To evaluate whether early NIV can reduce the need for invasive ventilation in patients with pneumonia-induced early mild ARDS. METHODS: Prospective, multicenter, randomized controlled trial (RCT) of NIV compared with conventional administration of oxygen through a Venturi mask. Primary outcome included the numbers of patients who met the intubation criteria. RESULTS: Two hundred subjects were randomized to NIV (n = 102) or control (n = 98) groups from 21 centers. Baseline characteristics were similar in the two groups. In the NIV group, PaO2/FIO2 became significantly higher than in the control group at 2 h after randomization and remained stable for the first 72 h. NIV did not decrease the proportion of patients requiring intubation than in the control group (11/102 vs. 9/98, 10.8% vs. 9.2%, p = 0.706). The ICU mortality was similar in the two groups (7/102 vs. 7/98, 4.9% vs. 3.1%, p = 0.721). Multivariate analysis showed minute ventilation greater than 11 L/min at 48 h was the independent risk factor for NIV failure (OR, 1.176 [95% CI, 1.005-1.379], p = 0.043). CONCLUSIONS: Treatment with NIV did not reduce the need for intubation among patients with pneumonia-induced early mild ARDS, despite the improved PaO2/FIO2 observed with NIV compared with standard oxygen therapy. High minute ventilation may predict NIV failure. TRIAL REGISTRATION: NCT01581229 . Registered 19 April 2012.

Discovering thematic change and evolution of utilizing social media for healthcare research.

BACKGROUND: Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric analysis to discover the thematic change and evolution of utilizing social media for healthcare research field. METHODS: With the basis of 4361 publications from both Web of Science and PubMed during the year 2008-2017, the analysis utilizes methods including topic modelling and science mapping analysis. RESULTS: Utilizing social media for healthcare research has attracted increasing attention from scientific communities. Journal of Medical Internet Research is the most prolific journal with the USA dominating in the research. Overly, major research themes such as YouTube analysis and Sex event are revealed. Themes in each time period and how they evolve across time span are also detected. CONCLUSIONS: This systematic mapping of the research themes and research areas helps identify research interests and how they evolve across time, as well as providing insight into future research direction.

An approach for transgender population information extraction and summarization from clinical trial text.

BACKGROUND: Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains the incompleteness and ambiguity issues in expressing transgender population, leading to difficulties or even failure of transgender population recruitment in clinical trial studies. METHODS: A new gender model is proposed for providing comprehensive transgender requirement specification. In addition, an automated approach is developed to extract and summarize gender requirements from unstructured text in accordance with the gender model. This approach consists of: 1) the feature extraction module, and 2) the feature summarization module. The first module identifies and extracts gender features using heuristic rules and automatically-generated patterns. The second module summarizes gender requirements by relation inference. RESULTS: Based on 100,134 clinical trials from ClinicalTrials.gov , our approach was compared with 20 commonly applied machine learning methods. It achieved a macro-averaged precision of 0.885, a macro-averaged recall of 0.871 and a macro-averaged F1-measure of 0.878. The results illustrated that our approach outperformed all baseline methods in terms of both commonly used metrics and macro-averaged metrics. CONCLUSIONS: This study presented a new gender model aiming for specifying the transgender requirement more precisely. We also proposed an approach for gender information extraction and summarization from unstructured clinical text to enhance transgender-related clinical trial population recruitment. The experiment results demonstrated that the approach was effective in transgender criteria extraction and summarization.

Factors Associated With Prolonged Viral Shedding in Patients With Avian Influenza A(H7N9) Virus Infection.

Background: Data are limited on the impact of neuraminidase inhibitor (NAI) treatment on avian influenza A(H7N9) virus RNA shedding. Methods: In this multicenter, retrospective study, data were collected from adults hospitalized with A(H7N9) infection during 2013-2017 in China. We compared clinical features and A(H7N9) shedding among patients with different NAI doses and combination therapies and evaluated factors associated with A(H7N9) shedding, using Cox proportional hazards regression. Results: Among 478 patients, the median age was 56 years, 71% were male, and 37% died. The median time from illness onset to NAI treatment initiation was 8 days (interquartile range [IQR], 6-10 days), and the median duration of A(H7N9) RNA detection from onset was 15.5 days (IQR, 12-20 days). A(H7N9) RNA shedding was shorter in survivors than in patients who died (P < .001). Corticosteroid administration (hazard ratio [HR], 0.62 [95% confidence interval {CI}, .50-.77]) and delayed NAI treatment (HR, 0.90 [95% CI, .91-.96]) were independent risk factors for prolonged A(H7N9) shedding. There was no significant difference in A(H7N9) shedding duration between NAI combination treatment and monotherapy (P = .65) or between standard-dose and double-dose oseltamivir treatment (P = .70). Conclusions: Corticosteroid therapy and delayed NAI treatment were associated with prolonged A(H7N9) RNA shedding. NAI combination therapy and double-dose oseltamivir treatment were not associated with a reduced A(H7N9) shedding duration as compared to standard-dose oseltamivir.

Photoacoustic and Magnetic Resonance Imaging Bimodal Contrast Agent Displaying Amplified Photoacoustic Signal.

Progress in photoacoustic (PA) and magnetic resonance imaging (MRI) bimodal contrast agents has been achieved mainly by utilizing the imaging capability of single or multiple components and consequently realizing the desired application for both imaging modalities. However, the mechanism of the mutual influence between components within a single nanoformulation, which is the key to developing high-performance multimodal contrast agents, has yet to be fully understood. Herein, by integrating conjugated polymers (CPs) with iron oxide (IO) nanoparticles using an amphiphilic polymer, a bimodal contrast agent named CP-IO is developed, displaying 45% amplified PA signal intensity as compared to bare CP nanoparticle, while the performance of MRI is not affected. Further experimental and theoretical simulation results reveal that the addition of IO nanoparticles in CP-IO nanocomposites contributes to this PA signal amplification through a synergistic effect of additional heat generation and faster heat dissipation. Besides, the feasibility of CP-IO nanocomposites acting as PA-MRI bimodal contrast agents is validated through in vivo tumor imaging using mice models. From this study, it is demonstrated that a delicately designed structural arrangement of various components in a contrast agent could potentially lead to a superior performance in the imaging capability.

A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.

BACKGROUND: Temporal expression extraction and normalization is a fundamental and essential step in clinical text processing and analyzing. Though a variety of commonly used NLP tools are available for medical temporal information extraction, few work is satisfactory for multi-lingual heterogeneous clinical texts. METHODS: A novel method called TEER is proposed for both multi-lingual temporal expression extraction and normalization from various types of narrative clinical texts including clinical data requests, clinical notes, and clinical trial summaries. TEER is characterized as temporal feature summarization, heuristic rule generation, and automatic pattern learning. By representing a temporal expression as a triple , TEER identifies temporal mentions M, assigns type attributes A to M, and normalizes the values of M into formal representations N. RESULTS: Based on two heterogeneous clinical text datasets: 400 actual clinical requests in English and 1459 clinical discharge summaries in Chinese. TEER was compared with six state-of-the-art baselines. The results showed that TEER achieved a precision of 0.948 and a recall of 0.877 on the English clinical requests, while a precision of 0.941 and a recall of 0.932 on the Chinese discharge summaries. CONCLUSIONS: An automated method TEER for multi-lingual temporal expression extraction was presented. Based on the two datasets containing heterogeneous clinical texts, the comparison results demonstrated the effectiveness of the TEER method in multi-lingual temporal expression extraction from heterogeneous narrative clinical texts.

Correction to: A pattern learning-based method for temporal expression extraction and normalization from multi-lingual heterogeneous clinical texts.

After publication of the original article [1] it was noted that the captions relating to Figs. 2 and 3 had been interchanged.

Deep learning architectures for multi-label classification of intelligent health risk prediction.

BACKGROUND: Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases. RESULTS: Physical examination records of 110,300 anonymous patients were used to predict diabetes, hypertension, fatty liver, a combination of these three chronic diseases, and the absence of disease (8 classes in total). The dataset was split into training (90%) and testing (10%) sub-datasets. Ten-fold cross validation was used to evaluate prediction accuracy with metrics such as precision, recall, and F-score. Deep Learning (DL) architectures were compared with standard and state-of-the-art multi-label classification methods. Preliminary results suggest that Deep Neural Networks (DNN), a DL architecture, when applied to multi-label classification of chronic diseases, produced accuracy that was comparable to that of common methods such as Support Vector Machines. We have implemented DNNs to handle both problem transformation and algorithm adaption type multi-label methods and compare both to see which is preferable. CONCLUSIONS: Deep Learning architectures have the potential of inferring more information about the patterns of physical examination data than common classification methods. The advanced techniques of Deep Learning can be used to identify the significance of different features from physical examination data as well as to learn the contributions of each feature that impact a patient's risk for chronic diseases. However, accurate prediction of chronic disease risks remains a challenging problem that warrants further studies.

Once-daily, oral levofloxacin monotherapy for low-risk neutropenic fever in cancer patients: a pilot study in China.

This pilot study assesses the safety and efficacy of once-daily, oral levofloxacin monotherapy in Chinese patients with low-risk febrile neutropenia. In this prospective, single-arm, open-label, multicenter clinical trial, 46 adult Chinese patients with solid tumors and low-risk febrile neutropenia were included. Patients received oral levofloxacin monotherapy (500 mg orally/day) until day 12, followed by 7 days of follow-up (day 19). Body temperature was measured three times per day. On days 2, 3, 5-7, 9, 12, and 19, disease symptoms and vital signs were recorded, adverse drug reactions were assessed, and blood samples were collected to determine the whole-blood cell count and the absolute neutrophil count. Blood cultures and chest radiographs were performed simultaneously until negative results were found. Oral levofloxacin was effective and well tolerated in 97.6% of patients irrespective of the cancer type and cause of fever. Body temperature began to decline in 24.4, 68.3, and 90.2% of patients, respectively, at 12, 24, and 48 h after initiating levofloxacin therapy. On days 5 and 7, 95.1 and 97.6% of the patients had complete defervescence, respectively. The median time for absolute neutrophil count recovery to at least 1500/mm after initiation of treatment was 3 days. Only one patient reported mild diarrhea. This pilot study showed that oral levofloxacin quickly and effectively reduced fever, initiated neutrophil recovery, and was well tolerated in Chinese low-risk febrile neutropenic patients with solid tumors. Further study is needed to compare patient data of levofloxacin with the standard amoxicillin/ciprofloxacin protocol in this population for both safety and efficacy.

The Effect of Gua Sha Therapy on Pain in Parkinson's Disease: a Randomized Controlled Trial.

Purpose: Pain is a common yet undertreated symptom of Parkinson's disease (PD). This study investigated the effect of Gua Sha therapy on pain in patients with PD. Patients and Methods: A total of 56 PD patients with pain were randomized into either the experimental group (n=28), receiving 12 sessions of Gua Sha therapy, or the control group (n=28) without additional treatment. Participants underwent assessment at baseline, after the twelfth invention, and at the 2-month follow-up timepoints. The primary outcome was KPPS and VAS. Secondary outcomes included UPDRS I-III, PDSS-2, HADS, PDQ-39, and blood biomarkers (5-HT, IL-8, IL-10). Results: The experimental group reported a significant improvement in pain severity, motor functions, affective disorder, and sleep quality (P < 0.05). Furthermore, increasing trends in both 5-HT and IL-10, as well as decreasing trends in IL-8 were observed. No serious adverse events occurred. Conclusion: The preliminary findings suggest that Gua Sha therapy may be effective and safe for alleviating pain and improving other disease-related symptoms in PD patients.

Clinical Application and Evaluation of Metagenomic Next-Generation Sequencing for Lower Respiratory Tract Infections and Human Tumor Screening.

Background: To evaluate the clinical value of metagenomic next-generation sequencing (mNGS) in screening of lower respiratory tract infections (LRTIs) and human tumors. Methods: Human samples included bronchoalveolar lavage fluid (BALF), sputum, lung biopsy tissue, and peripheral blood from 188 patients who were admitted to our hospital between January 2020 and September 2022 were analyzed using mNGS for simultaneous pathogen and chromosome copy number variation (CNV) detection. Traditional microbial culture and comprehensive microbial test (CMT) were also conducted. The diagnostic efficiencies of the three methods (mNGS, traditional culture, and CMT groups) were compared. Results: Among the 188 patients, 149 (79.3%) were in the LRTIs group and 39 (20.7%) were in the non-LRTIs group. The diagnostic sensitivity and accuracy of the mNGS group were higher than those of the traditional culture and CMT groups (P < 0.001; P < 0.001; P < 0.001; P < 0.001), and the specificity was higher than that of the CMT group (P = 0.039) but lower than that of the traditional culture group (P = 0.006). The positive predictive values of the mNGS and traditional culture groups were higher than that of the CMT group (P = 0.004; P = 0.011). The negative predictive value of the mNGS group was higher than that of the CMT group (P = 0.003). In addition, all samples were subjected to simultaneous chromosome CNV detection, and 8% (15/188) were positive for CNV. Of the 15 patients, 10 were initially misdiagnosed as non-neoplastic diseases, with a misdiagnosis rate of 66.7% (10/15). The BALF CNV test was performed on 13 patients diagnosed with primary or metastatic lung cancer, with a positivity rate of 38.5%. Conclusion: The sensitivity and accuracy of pathogen diagnosis using mNGS were better than those of traditional culture and CMT. CNV detection is an important auxiliary diagnostic tool for cancer, particularly for screening occult tumors.