Age at menarche and idiopathic pulmonary fibrosis: a two-sample mendelian randomization study.

BACKGROUND: Sex difference in the incidence rate of idiopathic pulmonary fibrosis (IPF) indicates that estrogen has a certain protective effect on the disease. Nevertheless, there is a dearth of study investigating the association between factors pertaining to endogenous estrogen exposure level, such as age at menarche (AAM) in women, and IPF. Our study intended to employ Mendelian randomization (MR) method to elucidate the causal association between AAM and IPF. METHODS: Our study utilized AAM as a measure of endogenous estrogen exposure and investigated its causal effect on the risk of IPF through MR. We employed the inverse variance weighted (IVW) method to assess the causal relationship between AAM and IPF risk, with supplementary analyses conducted using the weighted median estimator (WME) and MR-Egger method. Several sensitivity analyses were performed to assess the dependability of MR estimates. RESULTS: A total of 9 selected single nucleotide polymorphisms (SNPs) significantly associated with AAM were selected as instrumental variables. The IVW method showed that genetically later AAM was associated with an increased risk of IPF (odds ratio [OR] = 1.0014, 95%confidence interval [CI] = 1.0005-1.0023, p = 0.001). The median weighting method and the MR-Egger method obtained similar estimates, and no heterogeneity or pleiotropy was found, indicating that the results were robust. CONCLUSIONS: Our MR study suggested a causal relationship between a later onset of menarche and a heightened susceptibility to IPF.

A Strategy for the Effective Optimization of Pharmaceutical Formulations Based on Parameter-Optimized Support Vector Machine Model.

Engineering pharmaceutical formulations is governed by a number of variables, and the finding of the optimal preparation is intricately linked to the exploration of a multiparametric space through a variety of optimization tasks. As a result, making such optimization activities simpler is a significant undertaking. For the purposes of this study, we suggested a prediction model that was based on least square support vector machine (LSSVM) and whose parameters were optimized using the particle swarm optimization algorithm (PSO-LSSVM model). Other in silico optimization methods were used and compared, including the LSSVM and the back propagation (BP) neural networks algorithm. PSO-LSSVM demonstrated the highest performance on the test dataset, with the lowest mean square error. In addition, two dosage forms, quercetin solid dispersion and apigenin nanoparticles, were selected as model formulations due to the wide range of formulation compositions and manufacturing factors used in their production. Three different models were used to predict the ideal formulations of two different dosage forms, and in real world, the Taguchi orthogonal design arrays were used to optimize the formulations of each dosage form. It is clear that the predicted performance of two formulations using PSO-LSSVM was both consistent with the outcomes of the Taguchi orthogonal planned experiment, demonstrating the model's good reliability and high usefulness. Together, our PSO-LSSVM prediction model has the potential to accurately predict the best possible formulations, reduce the reliance on experimental effort, accelerate the process of formulation design, and provide a low-cost solution to drug preparation optimization.

Clinical features of cryptococcosis in patients with different immune statuses: a multicenter study in Jiangsu Province-China.

BACKGROUND: Current guidelines support different management of cryptococcosis between severely immunodeficient and immunocompetent populations. However, few studies have focused on cryptococcosis patients with mild-to-moderate immunodeficiency. We performed this study to determine the clinical features of pulmonary (PC) and extrapulmonary cryptococcosis (EPC) and compared them among populations with different immune statuses to support appropriate clinical management of this public health threat. METHODS: All cases were reported by 14 tertiary teaching hospitals in Jiangsu Province, China from January 2013 to December 2018. The trends in incidence, demographic data, medical history, clinical symptoms, laboratory test indicators, imaging characteristics and diagnostic method of these patients were then stratified by immune status, namely immunocompetent (IC, patients with no recognized underlying disease or those with an underlying disease that does not influence immunity, such as hypertension), mild-to-moderate immunodeficiency (MID, patients with diabetes mellitus, end-stage liver or kidney disease, autoimmune diseases treated with low-dose glucocorticoid therapy, and cancer treated with chemotherapy) and severe immunodeficiency (SID, patients with acquired immunodeficiency syndrome, haematologic malignancies, solid organ transplantation or haematologic stem cell transplantation, idiopathic CD4 lymphocytosis, agranulocytosis, aggressive glucocorticoid or immunosuppressive therapy and other conditions or treatments that result in severe immunosuppression). RESULTS: The clinical data of 255 cryptococcosis patients were collected. In total, 66.3% of patients (169) were IC, 16.9% (43) had MID, and 16.9% (43) had SID. 10.1% of the patients (17) with IC were EPC, 18.6% of the patients (8) with MID were EPC, and 74.4% of patients (32) were EPC (IC/MID vs. SID, p < 0.001). Fever was more common in the SID group than in the IC and MID groups (69.8% vs. 14.8% vs. 37.2%, p < 0.001). Of chest CT scan, most lesions were distributed under the pleura (72.7%), presenting as nodules/lumps (90.3%) or consolidations (10.7%). Pleural effusion was more common in SID group compared to IC group (33.3% vs. 2.4%, p < 0.001). Positivity rate on the serum capsular polysaccharide antigen detection (CrAg) test was higher in the SID group than in the other two groups [100.0% vs. 84.4% (MID) vs. 78.2% (IC), p = 0.013]. Positivity rate on the serum CrAg test was also higher in cryptococcal meningitis patients than in PC patients (100.0% vs. 79.5%, p = 0.015). CONCLUSIONS: The clinical presentation of MID patients is intermediate between SID and IC patients and is similar to that of IC patients. The serum CrAg test is more sensitive for the identification of SID or EPC patients.

The role of pentraxin3 in plasma and bronchoalveolar lavage fluid in COPD patients with invasive pulmonary aspergillosis.

BACKGROUND: The use of galactomannan (GM) testing in plasma and bronchoalveolar lavage fluid (BALF) has improved the diagnosis of invasive pulmonary aspergillosis (IPA) in patients with chronic obstructive pulmonary disease (COPD); however, the high false-positive rate leads to overdiagnosis. Pentraxin 3 (PTX3) as an indicator of inflammation plays an important role in resistance to Aspergillus infections. This study aimed to investigate the diagnostic value of PTX3 for diagnosing IPA with COPD. METHODS: We retrospectively collected data on patients with suspected COPD and IPA who had been hospitalized in the Third Affiliated Hospital of Soochow University between September 2017 and November 2020. PTX3 and GM were measured using enzyme-linked immunosorbent assays. RESULTS: A total of 165 patients were included in the study, of whom 35 had confirmed or probable IPA. The remaining 130 patients served as controls. The median plasma and BALF PTX3 levels were significantly higher in patients with IPA than in control patients (3.74 ng/mL vs. 1.29 ng/mL, P < 0.001; and 3.88 ng/mL vs. 1.58 ng/mL, P < 0.001 in plasma and BALF, respectively). The plasma GM, plasma PTX3, BALF GM, and BALF PTX3 assays had sensitivities of 60.0%, 77.1%, 78.6%, and 89.3%, respectively, and specificities of 73.8%, 69.2%, 80.7%, and 77.1%, respectively. The sensitivity of PTX3 in plasma and BALF was higher than that of GM. However, the specificity of PTX3 and GM did not differ significantly between the IPA group and the control group. The specificity of the assays for the diagnosis of IPA was > 90% in patients who were PTX3-positive and GM-positive in plasma and BALF. CONCLUSIONS: BALF and plasma PTX3 levels were significantly higher in COPD patients with IPA. The sensitivity of PTX3 was superior to that of GM for diagnosing IPA in patients with COPD. The combination of GM and PTX3 is useful for the diagnosis of IPA in patients with COPD.

Therapeutic effects of pentoxifylline on invasive pulmonary aspergillosis in immunosuppressed mice.

BACKGROUND: The most common and severe infection of Aspergillus fumigatus is invasive pulmonary aspergillosis (IPA), which is usually seen in immunocompromised patients. Neutropenia is the primary risk factor implicated in IPA; however, IPA also occurs in patients without neutropenia, namely, those who are immunosuppressed owing to long-term corticosteroid use. With IPA-associated mortality as high as 51-79%, novel and effective treatment strategies are urgently needed. Pentoxifylline (PTX) has been shown to competitively inhibit the family 18 chitinases in fungi, which may be an new antifungal therapy. Hence, the aim of our study was to compare neutropenic and non-neutropenic IPA mouse models, and to evaluate the effect of PTX on IPA in immunosuppressed mice. METHODS: C57BL/6J mice were pre-treated with cyclophosphamide and hydrocortisone. Neutropenic model IPA mice (CTX-IPA) and non-neutropenic IPA mice (HC-IPA) were established by intranasal administration of Aspergillus fumigatus spore suspension. A subset of each group was injected with PTX post-infection. Among these groups, we compared overall survival, pulmonary fungal burden, lung hispathology, and myeloperoxidase (MPO), interleukin 8 (IL-8), and mammalian chitinase concentration in the bronchoalveolar lavage fluid (BALF). RESULTS: The survival rate of the HC-IPA group was higher than that of the CTX-IPA group, and pulmonary fungal burden was also lower (p < 0.05). The CTX-IPA group showed infiltration of alveolae and blood vessels by numerous hyphae of A. fumigatus. The HC-IPA group exhibited destruction of bronchi, expansion of alveolar septa, increased macrophages aggregation, significant neutrophil infiltration and a few hyphae in peribronchial areas. After PTX treatment, improvement was observed in survival duration and pulmonary fungal burden in HC-IPA mice. MPO and IL-8 levels were lower in the HC-IPA + PTX group compared to the corresponding levels in the HC-IP group. Chitotriosidase (CHIT1) and Chitinase 3-like 1 (CHI3L1) expression in the HC-IPA group was decreased after PTX treatment (p < 0.05). CONCLUSION: PTX was found to exert a therapeutic effect in a non-neutropenic mouse model of IPA, which may lead to the development of novel strategies for IPA treatment.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance.

Drug-induced liver injury (DILI), one of the most common adverse effects, leads to drug development failure or withdrawal from the market in most cases, showing an emerging challenge that is to accurately predict DILI in the early stage. Recently, the vast amount of gene expression data provides us valuable information for distinguishing DILI on a genomic scale. Moreover, the deep learning algorithm is a powerful strategy to automatically learn important features from raw and noisy data and shows great success in the field of medical diagnosis. In this study, a gene expression data based deep learning model was developed to predict DILI in advance by using gene expression data associated with DILI collected from ArrayExpress and then optimized by feature gene selection and parameters optimization. In addition, the previous machine learning algorithm support vector machine (SVM) was also used to construct another prediction model based on the same data sets, comparing the model performance with the optimal DL model. Finally, the evaluation test using 198 randomly selected samples showed that the optimal DL model achieved 97.1% accuracy, 97.4% sensitivity, 96.8% specificity, 0.942 matthews correlation coefficient, and 0.989 area under the ROC curve, while the performance of SVM model only reached 88.9% accuracy, 78.8% sensitivity, 99.0% specificity, 0.794 matthews correlation coefficient, and 0.901 area under the ROC curve. Furthermore, external data sets verification and animal experiments were conducted to assess the optimal DL model performance. Finally, the predicted results of the optimal DL model were almost consistent with experiment results. These results indicated that our gene expression data based deep learning model could systematically and accurately predict DILI in advance. It could be a useful tool to provide safety information for drug discovery and clinical rational drug use in early stage and become an important part of drug safety assessment.

Targeted Biomimetic Nanoparticles for Synergistic Combination Chemotherapy of Paclitaxel and Doxorubicin.

Codelivery of multiple chemotherapeutics has become a versatile strategy in recent cancer treatment, but the antagonistic behavior of combined drugs limited their application. We developed a recombinant high-density lipoprotein (rHDL) nanoparticle for the precise coencapsulation and codelivery of two established drugs and hypothesized that they could act synergistically to improve anticancer efficacy. The coloaded rHDL was formulated by passively incorporating hydrophobic paclitaxel (PTX), and subsequently remotely loading hydrophilic doxorubicin (Dox) into the same nanoparticles. The resultant rHDL system restored targeted delivery function toward cancer cells via scavenger receptor class B (SR-BI), as confirmed by in vitro confocal imaging and flow cytometry. These coloaded rHDL nanoparticles were remarkably effective in increasing the ratiometric accumulation of drugs in cancer cells and enhancing antitumor response at synergistic drug ratios. In particular, they exhibited more efficacious anticancer effects in an in vitro cytotoxicity evaluation and in a xenograft tumor model of hepatoma compared with free drug cocktail solutions. These results confirm that the coloaded rHDL nanoparticles are promising candidates for the synergistic delivery of drugs with diverse physicochemical properties in cancer treatment integrating efficiency and safety considerations.

IL-17 in plasma and bronchoalveolar lavage fluid in non-neutropenic patients with invasive pulmonary aspergillosis.

Background: The purpose of this study was to investigate the diagnostic value of IL-17 detection in bronchoalveolar lavage fluid (BALF) and plasma samples from nonneutropenic patients with invasive pulmonary aspergillosis. Methods: We retrospectively collected data on non-neutropenic patients who were suspected to have IPA admitted to the Third Affiliated Hospital of Soochow University between March 2020 to January 2023. IL-17 and GM were measured using enzyme-linked immunosorbent assays. Results: A total of 281 patients were enrolled in this study, of which 62 had proven or probable IPA and the remaining 219 patients were controls. The plasma and BALF IL-17 levels were significantly higher in the IPA group compared with the control group. The plasma GM, plasma IL17, BALF GM, and BALF IL17 assays had sensitivities of 56.5%, 72.6%, 68.7%, and 81.2%, respectively, and specificities of 87.7%, 69.4%, 91.9%, and 72.6%, respectively. The sensitivity of IL17 in plasma and BALF was higher than that of GM. Plasma GM in combination with IL-17 increases the sensitivity but does not decrease the diagnostic specificity of GM testing. The diagnostic sensitivity and specificity of BALF GM combined with IL-17 for IPA in non-neutropenic patients were greater than 80% and there was a significant increase in sensitivity compared with BALF GM. Conclusions: Plasma and BALF IL-17 levels were significantly higher in non-neutropenic patients with IPA. The sensitivity of plasma and BLAF IL-17 for diagnosing IPA in non-neutropenic patients was superior to that of GM. Combined detection of lavage fluid GM and IL17 significantly improves the diagnosis of IPA in non-neutropenic patients. The combined detection of GM and IL-17 in plasma also contributes to the diagnosis of IPA in patients who cannot tolerate invasive procedures.

Value of rapid on-site evaluation combined with interventional pulmonology techniques in the diagnosis of pulmonary cryptococcosis.

OBJECTIVES: The aim of this study is to evaluate the diagnostic value of rapid on-site evaluation (ROSE) combined with computed tomography-guided percutaneous needle biopsy (CT-PNB) or radial endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB) for pulmonary cryptococcosis (PC). METHODS: Clinical data of 33 patients diagnosed with PC at the Third Affiliated Hospital of Soochow University between February 2018 and June 2023 were retrospectively analysed. Patients were divided into the CT-PNB and EBUS-TBLB groups based on the intervention method, and the diagnostic positivity rate and incidence of complications were compared between the two groups. RESULTS: Compared with the final diagnosis, the positive diagnostic rates of ROSE, histopathology and serum CrAg of all patients were 81.8% (27/33), 72.7% (24/33) and 63.6% (21/33), respectively. The average turnaround times of the three methods were 0.1 (0.1-0.2) h, 96.0 (48.0-120.0) h and 7.8 (4.5-13.6) h, respectively (P < 0.001). The coincidence rate between histopathology and ROSE was 84.8% with a kappa value of 0.574. The positive diagnostic rate for PC was significantly higher in the CT-PNB group than in the EBUS-TBLB group (92.9% vs. 57.9%), and the difference was statistically significant (P < 0.05). Combined with the ROSE results, the positive diagnostic rate in the EBUS-TBLB group increased to 84.2% (16/19). CONCLUSION: ROSE has commendable accuracy and timeliness, and CT-PNB offers further advantages in this regard. ROSE enhances the diagnostic efficiency of EBUS-TBLB for PC and is safe and effective.

A general prediction model for compound-protein interactions based on deep learning.

Background: The identification of compound-protein interactions (CPIs) is crucial for drug discovery and understanding mechanisms of action. Accurate CPI prediction can elucidate drug-target-disease interactions, aiding in the discovery of candidate compounds and effective synergistic drugs, particularly from traditional Chinese medicine (TCM). Existing in silico methods face challenges in prediction accuracy and generalization due to compound and target diversity and the lack of largescale interaction datasets and negative datasets for model learning. Methods: To address these issues, we developed a computational model for CPI prediction by integrating the constructed large-scale bioactivity benchmark dataset with a deep learning (DL) algorithm. To verify the accuracy of our CPI model, we applied it to predict the targets of compounds in TCM. An herb pair of Astragalus membranaceus and Hedyotis diffusaas was used as a model, and the active compounds in this herb pair were collected from various public databases and the literature. The complete targets of these active compounds were predicted by the CPI model, resulting in an expanded target dataset. This dataset was next used for the prediction of synergistic antitumor compound combinations. The predicted multi-compound combinations were subsequently examined through in vitro cellular experiments. Results: Our CPI model demonstrated superior performance over other machine learning models, achieving an area under the Receiver Operating Characteristic curve (AUROC) of 0.98, an area under the precision-recall curve (AUPR) of 0.98, and an accuracy (ACC) of 93.31% on the test set. The model's generalization capability and applicability were further confirmed using external databases. Utilizing this model, we predicted the targets of compounds in the herb pair of Astragalus membranaceus and Hedyotis diffusaas, yielding an expanded target dataset. Then, we integrated this expanded target dataset to predict effective drug combinations using our drug synergy prediction model DeepMDS. Experimental assay on breast cancer cell line MDA-MB-231 proved the efficacy of the best predicted multi-compound combinations: Combination I (Epicatechin, Ursolic acid, Quercetin, Aesculetin and Astragaloside IV) exhibited a half-maximal inhibitory concentration (IC50) value of 19.41 µM, and a combination index (CI) value of 0.682; and Combination II (Epicatechin, Ursolic acid, Quercetin, Vanillic acid and Astragaloside IV) displayed a IC50 value of 23.83 µM and a CI value of 0.805. These results validated the ability of our model to make accurate predictions for novel CPI data outside the training dataset and evaluated the reliability of the predictions, showing good applicability potential in drug discovery and in the elucidation of the bioactive compounds in TCM. Conclusion: Our CPI prediction model can serve as a useful tool for accurately identifying potential CPI for a wide range of proteins, and is expected to facilitate drug research, repurposing and support the understanding of TCM.

In silico screening and in vivo experimental validation of 15-PGDH inhibitors from traditional Chinese medicine promoting liver regeneration.

The enzyme 15-hydroxyprostaglandin dehydrogenase (15-PGDH), which acts as a negative regulator of prostaglandin E2 (PGE2) levels and activity, represents a promising pharmacological target for promoting liver regeneration. In this study, we collected data on 15-PGDH homologous family proteins, their inhibitors, and traditional Chinese medicine (TCM) compounds. Leveraging machine learning and molecular docking techniques, we constructed a prediction model for virtual screening of 15-PGDH inhibitors from TCM compound library and successfully screened genistein as a potential 15-PGDH inhibitor. Through further validation, it was discovered that genistein considerably enhances liver regeneration by inhibiting 15-PGDH, resulting in a significant increase in the PGE2 level. Genistein's effectiveness suggests its potential as a novel therapeutic agent for liver diseases, highlighting this study's contribution to expanding the clinical applications of TCM.

Optimizing treatment administration strategies using negative mNGS results in corticosteroid-sensitive diffuse parenchymal lung diseases.

Timely adjustments of antibiotic and corticosteroid treatments are vital for patients with diffuse parenchymal lung diseases (DPLDs). In this study, 41 DPLD patients with negative metagenomic next-generation sequencing (mNGS) results who were responsive to corticosteroids were enrolled. Among these patients, about 26.8% suffered from drug-induced DPLD, while 9.8% presented autoimmune-related DPLD. Following the report of the negative mNGS results, in 34 patients with complete antibiotics administration profiles, 79.4% (27/34) patients discontinued antibiotics after receiving negative mNGS results. Moreover, 70.7% (29/41) patients began or increased the administration of corticosteroid upon receipt of negative mNGS results. In the microbiota analysis, Staphylococcus and Stenotrophomonas showed higher detection rates in patients with oxygenation index (OI) below 300, while Escherichia and Stenotrophomonas had higher abundance in patients with pleural effusion. In summary, our findings demonstrated the clinical significance of mNGS in assisting the antibiotic and corticosteroid treatment adjustments in corticosteroid-responsive DPLD. Lung microbiota may imply the severity of the disease.

Design and evaluation of α-helix-based peptide inhibitors for blocking PD-1/PD-L1 interaction.

The current research in tumor immunotherapy indicates that blocking the protein-protein interaction (PPI) between PD-1 and its ligand, PD-L1, may be one of the most effective treatments for cancer patients. The α-helix is a common elements of protein secondary structure and is often involved in protein interaction. Thus, α-helix-based peptides could mimic proteins involved in such interactions and are also capable of modulating PPI in vivo. In this study, starting from a potential α-helix-rich protein, we designed a series of α-helix-based peptide candidates to block PD-1/PD-L1 interaction. These candidates were first screened using molecular docking and molecular dynamics simulations, and then their capacities to inhibit PD-1/PD-L1 interactions and to restore antitumor immune activities were investigated using the HTRF assay, SPR assay, cellular co-culture experiments and animal model experiments. Two peptides exhibited the best anti-tumor effects and the strong ability to restore the immunity of tumor-infiltrating T-cells. Further D-amino acid substitution was employed to improve the serum stability of peptide candidate, making the intravenous administration easier while maintaining the therapeutic efficacy. The resultant peptides showed promise as checkpoint inhibitors for application in tumor immunotherapy. These findings suggested that our strategy for developing peptides starting from an α-helical structure could be used in the design of bioactive inhibitors to potential block protein-protein interactions.

The management and outcome of cryptococcosis in patients with different immune statuses and treatment protocols: A multicenter real-world study in Jiangsu Province - China.

OBJECTIVE: The incidence of cryptococcosis is increasing in non-immunocompromised patients. However, the evidence on proper management is inadequate in this population. We conducted this multi-center real-world study in pulmonary cryptococcosis patients with different immune statuses, so as to provide practical evidence for optimized clinical management of cryptococcosis, especially for mild-to-moderate immunodeficient diseases patients. METHODS: This is a prospective observational study. The clinical data of patients with proven cryptococcosis were collected and analyzed from 7 tertiary teaching hospitals in Jiangsu Province, China from January, 2013 to December, 2018. Proven cases include pulmonary cryptococcosis, cryptococcal meningitis, cryptococcemia and cutaneous cryptococcosis. Patients were followed up over 24 months. According to their immune status, patients with cryptococcosis were divided into three groups, namely immunocompetent group (IC), mild-to-moderate immunodeficient diseases group (MID), severe immunodeficient diseases group (SID). Meanwhile, pulmonary crypotococcosis (PC) and extrapulmonary crypotococcosis (EPC) were also classified and analyzed. RESULTS: 255 proven cases of cryptococcosis were enrolled. Finally, 220 cases completed the follow-up. 143 proven cases (65.0%) were immunocompetent (IC), 41 cases (18.6%) were MID, and 36 cases (16.4%) were SID. 174 cases (79.1%) were PC and 46 cases (20.9%) were EPC. The mortality was significantly higher in SID and MID patients [47.2% (SID) vs. 12.2% (MID) vs. 0.0% (IC), p<0.001]. The mortality was also significantly higher in EPC patients [45.7% vs. 0.6% (PC), p<0.001]. Patients with alternative initial antifungal treatment had higher mortality than patients with guideline recommended initial treatment [23.1% vs. 9.5%, p=0.041]. In MID group, the mortality of receiving alternative initial antifungal treatment was significantly higher than recommended initial treatment [2/3 vs. 3/34(8.8%), p=0.043]. In pulmonary cryptococcosis patients with MID, the mortality was very similar to IC group [0.0% vs. 0.0% (IC)], lower than SID group [0.0% vs. 11.1% (SID), p=0.555]. However, in extrapulmonary cryptococcosis patients with MID, the mortality was significantly higher than that in IC [62.5% vs. 0.0% (IC)], and similar to SID patients [62.5% vs. 59.3% (SID)]. CONCLUSION: The immune status exert a significant influence on the management and prognosis of cryptococcosis patients. The mortality of cryptococcosis patients with MID is higher than that of immunocompetent patients. For MID patients with pure pulmonary cryptococcosis, it is acceptable to take the treatment recommended as IC patients. For the MID patients with extrapulmonary cryptococcosis, the mortality is high and the initial treatment should follow the regimen for SID patients. Following the recommended treatment regimen in the IDSA guideline can reduce mortality in patients with cryptococcosis. Starting on alternative initial antifungal treatment may bring worse outcomes.

Clinical Features, Treatment, and Outcome of Psittacosis Pneumonia: A Multicenter Study.

Background: We aimed to describe psittacosis pneumonia and risk factors for developing severe pneumonia in this multicenter clinical study. Methods: We collected the data of psittacosis pneumonia cases diagnosed with metagenomic next-generation sequencing (mNGS) assay from April 2018 to April 2022 in 15 tertiary hospitals in China. Results: A total of 122 patients were enrolled; 50.0% had a definite history of bird exposure. In 81.2% of cases, onset happened in autumn or winter. The common symptoms were fever (99.2%), cough (63.1%), fatigue (52.5%), shortness of breath (50.0%), chills (37.7%), central nervous system symptoms (36.9%), myalgia (29.5%), and gastrointestinal tract symptoms (15.6%). Laboratory tests showed that >70% of cases had elevated C-reactive protein, procalcitonin, erythrocyte sedimentation rate, D-dimer, lactate dehydrogenase, and aspartate aminotransferase, and >50% had hyponatremia and hypoproteinemia. The most common imaging finding was consolidation (71.3%), and 42.6% of cases met the criteria for severe pneumonia. Age >65 years and male sex were the risk factors for severe pneumonia. The effective proportion of patients treated with tetracyclines was higher than that of fluoroquinolones (66/69 [95.7%] vs 18/58 [31.0%]; P < .001), and the median defervescence time was shorter. After medication adjustment when the diagnosis was clarified, 119 of 122 (97.5%) patients were finally cured and the other 3 (2.5%) died. Conclusions: Psittacosis pneumonia has a high rate of severe disease. Proven diagnosis could be rapidly confirmed by mNGS. Tetracycline therapy had a rapid effect and a high cure rate.

The clinical value of Aspergillus-specific IgG antibody test in the diagnosis of nonneutropenic invasive pulmonary aspergillosis.

OBJECTIVES: Aspergillus-specific IgG antibody (Asp IgG) has been successfully applied in the diagnosis of chronic pulmonary aspergillosis. We explored its value in nonneutropenic invasive pulmonary aspergillosis (IPA) by a multicenter, prospective, and controlled study. METHODS: We enrolled 372 clinically suspected nonneutropenic patients with IPA from February 2015 to August 2022. After excluding 4 cases with Aspergillus colonization, the remaining 368 cases were finally confirmed as patients with IPA (n = 99), or non-IPA patients (n = 269) consisting of community-acquired pneumonia (n = 206), tuberculosis (n = 22), nontuberculous mycobacteria (n = 5), lung abscess (n = 6), or noninfectious diseases (n = 30). Asp IgG in plasma samples was tested by enzyme-linked immunosorbent assay. RESULTS: At cut-off value of ≥80 AU/mL, Asp IgG had much higher sensitivity (59.6% vs. 19.2%, p < 0.0001), but lower specificity (77.0% vs. 96.3%, p < 0.0001) than serum galactomannan (GM) (cut-off value of ≥1.0), and similar sensitivity (59.6% vs. 55.6%, p = 0.611) but lower specificity (77.0% vs. 91.2%, p = 0.001) than bronchoalveolar lavage fluid (BALF) GM (cut-off value of ≥1.0), respectively. Combination diagnosis of either positive for Asp IgG or BALF GM had higher sensitivity (81.0% vs. 55.6%, p = 0.002), but lower specificity (75.2% vs. 91.2%, p = 0.001) than BALF GM alone. The receiver operating characteristic curve showed that Asp IgG had an optimal diagnostic value when the cut-off value was 56.6 AU/ml, and the sensitivity and specificity were 77.8% and 63.9%, respectively. DISCUSSIONS: The diagnostic value of Asp IgG for IPA is superior to serum GM, and a little inferior to BALF GM in nonneutropenic patients with IPA. Considering the convenience of taking blood samples, it is a good screening and diagnostic method for nonneutropenic patients with IPA, especially for those who cannot bear invasive procedures.

Diagnosis values of Dectin-1 and IL-17 levels in plasma for invasive pulmonary aspergillosis in bronchiectasis.

Background: Among immunocompetent patients, patients with bronchiectasis are considered to be a high-risk group for invasive pulmonary aspergillosis (IPA). Early diagnosis and treatment can improve the prognosis of patients. Objectives: We aimed to investigate the diagnostic value of Dectin-1 and IL-17 for diagnosing IPA with bronchiectasis. Methods: We retrospectively collected data on patients with bronchiectasis who had been hospitalized in the Third Affiliated Hospital of Soochow University between September 2018 to December 2021. Dectin-1, IL-17 and GM were measured by enzyme-linked immunosorbent assays. Results: A total of 129 patients were analyzed in the study, of whom 33 had proven or probable IPA with bronchiectasis. The remaining 96 patients served as controls. The plasma Dectin-1 and IL-17 levels in the IPA group were significantly higher than that in the control group (P=0.005; P<0.001). The plasma GM, BALF GM, plasma Dectin-1 and IL-17 assays had sensitivities of 39.4%, 62.5%, 69.7% and 78.8%, respectively, and specificities of 89.2%, 91.5%, 72.9% and 71.9%, respectively. The sensitivity of Dectin-1 and IL-17 in plasma was higher than that in plasma and BALF GM. while the specificity is lower than that of plasma and BALF GM. The diagnostic sensitivity and specificity of plasma GM combined with IL-17 for IPA in bronchiectasis were greater than 80%. The combination of plasma GM and IL-17 can improve the sensitivity of the GM test, but does not reduce the diagnostic specificity. The plasma Dectin-1 and IL-17 showed positive linear correlations with the bronchiectasis severity Index (BSI) score in linear regression. Conclusions: Plasma Dectin-1 and IL-17 levels were significantly higher in bronchiectasis patients with IPA. The sensitivity of Dectin-1 and IL-17 was superior to that of GM for the diagnosis of IPA in patients with bronchiectasis. The combination of GM and IL-17 in plasma is helpful for the diagnosis of IPA in bronchiectasis patients who cannot tolerate invasive procedures.

In Silico Screening and Validation of PDGFRA Inhibitors Enhancing Radioiodine Sensitivity in Thyroid Cancer.

Aberrant activation of platelet-derived growth factor receptor α (PDGFRA) has been implicated in tumorigenesis and radioiodine resistance of thyroid cancer, indicating its therapeutic potential. In the present study, we confirmed the association between PDGFRA and radioiodine resistance in thyroid cancer using bioinformatics analysis and constructed a prediction model of PDGFRA inhibitors using machine learning and molecular docking approaches. We then performed a virtual screening of a traditional Chinese medicine (TCM) derived compound library and successfully identified 4',5,7-trimethoxyflavone as a potential PDGFRA inhibitor. Further characterization revealed a significant inhibitory effect of 4',5,7-trimethoxyflavone on PDGFRA-MAPK pathway activation, and that it could upregulate expression of sodium iodide symporter (NIS) as well as improve radioiodine uptake capacity of radioiodine-refractory thyroid cancer (RAIR-TC), suggesting it a potential drug lead for the development of new RAIR-TC therapy.

Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies.

While synergistic drug combinations are more effective at fighting tumors with complex pathophysiology, preference compensating mechanisms, and drug resistance, the identification of novel synergistic drug combinations, especially complex higher-order combinations, remains challenging due to the size of combination space. Even though certain computational methods have been used to identify synergistic drug combinations in lieu of traditional in vitro and in vivo screening tests, the majority of previously published work has focused on predicting synergistic drug pairs for specific types of cancer and paid little attention to the sophisticated high-order combinations. The main objective of this study is to develop a deep learning-based approach that integrated multi-omics data to predict novel synergistic multi-drug combinations (DeepMDS) in a given cell line. To develop this approach, we firstly created a dataset comprising of gene expression profiles of cancer cell lines, target information of anti-cancer drugs, and drug response against a large variety of cancer cell lines. Based on the principle of a fully connected feed forward Deep Neural Network, the proposed model was constructed using this dataset, which achieved a high performance with a Mean Square Error (MSE) of 2.50 and a Root Mean Squared Error (RMSE) of 1.58 in the regression task, and gave the best classification accuracy of 0.94, an area under the Receiver Operating Characteristic curve (AUC) of 0.97, a sensitivity of 0.95, and a specificity of 0.93. Furthermore, we utilized three breast cancer cell subtypes (MCF-7, MDA-MD-468 and MDA-MB-231) and one lung cancer cell line A549 to validate the predicted results of our model, showing that the predicted top-ranked multi-drug combinations had superior anti-cancer effects to other combinations, particularly those that were widely used in clinical treatment. Our model has the potential to increase the practicality of expanding the drug combinational space and to leverage its capacity to prioritize the most effective multi-drug combinational therapy for precision oncology applications.

Prediction model for synergistic anti-tumor multi-compound combinations from traditional Chinese medicine based on extreme gradient boosting, targets and gene expression data.

Traditional Chinese medicine (TCM) is characterized by synergistic therapeutic effect involving multiple compounds and targets, which provide potential new therapy for the treatment of complex cancer conditions. However, the main contributors and the underlying mechanisms of synergistic TCM cancer therapies remain largely undetermined. Machine learning now provides a new approach to determine synergistic compound combinations from complex components of TCM. In this study, a prediction model based on extreme gradient boosting (XGBoost) algorithm was constructed by integrating gene expression data of different cancer cell lines, targets information of natural compounds and drug response data. Radix Paeoniae Rubra (RPR) was selected as a model herbal sample to evaluate the reliability of the constructed model. The optimal XGBoost prediction model achieved a good performance with Mean Square Error (MSE) of 0.66, Mean Absolute Error (MAE) of 0.61, and the Root Mean Squared Error (RMSE) of 0.81 on test dataset. The superior synergistic anti-tumor combinations of D15 (Paeonol[Formula: see text][Formula: see text][Formula: see text]Ethyl gallate) and D13 (Paeoniflorin[Formula: see text][Formula: see text][Formula: see text]Paeonol) were successfully predicted from RPR and experimentally validated on MCF-7 cells. Moreover, the combination of D13 could work as a main contributor to a synergistic anti-proliferative activity in the compatibility of RPR and Cortex Moutan (CM). Our XGBoost model could be a reliable tool for the efficient prediction of synergistic anti-tumor multi-compound combinations from TCM.