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Purpose: To predict prognosis in HIV-negative cryptococcal meningitis (CM) patients by developing and validating a machine learning (ML) model. Methods: This study involved 523 HIV-negative CM patients diagnosed between January 1, 1998, and August 31, 2022, by neurologists from 3 tertiary Chinese centers. Prognosis was evaluated at 10 weeks after the initiation of antifungal therapy. Results: The final prediction model for HIV-negative CM patients comprised 8 variables: Cerebrospinal fluid (CSF) cryptococcal count, CSF white blood cell (WBC), altered mental status, hearing impairment, CSF chloride levels, CSF opening pressure (OP), aspartate aminotransferase levels at admission, and decreased rate of CSF cryptococcal count within 2 weeks after admission. The areas under the curve (AUCs) in the internal, temporal, and external validation sets were 0.87 (95% CI 0.794–0.944), 0.92 (95% CI 0.795–1.000), and 0.86 (95% CI 0.744–0.975), respectively. An artificial intelligence (AI) model was trained to detect and count cryptococci, and the mean average precision (mAP) was 0.993. Conclusion: A ML model for predicting prognosis in HIV-negative CM patients was built and validated, and the model might provide a reference for personalized treatment of HIV-negative CM patients. The change in the CSF cryptococcal count in the early phase of HIV-negative CM treatment can reflect the prognosis of the disease. In addition, utilizing AI to detect and count CSF cryptococci in HIV-negative CM patients can eliminate the interference of human factors in detecting cryptococci in CSF samples and reduce the workload of the examiner. [ABSTRACT FROM AUTHOR]

Background: While new genetic analysis methods are widely used in the clinic, few researchers have focused on trigeminal neuralgia (TN) with familial clustering (≥ 2 TN patients in one kindred family). Previous literature suggests that familial trigeminal neuralgia (FTN) may be associated with inherited genetic factors. To date, few next-generation sequencing studies have been reported for FTN. This study investigated the pathogenic mechanism of FTN by using whole-exome sequencing (WES) technology, which may enhance our understanding of human TN pathophysiology. Method: We performed WES for 7 probands from families of FTN. Sanger sequencing was performed for two control groups (FTN family members group and nonfamilial TN subject group) to potentially identify new FTN-related gene mutations. In families where FTN probands carried potentially pathogenic gene mutations, the ribonucleic acid (RNA) of FTN probands and related family members, as well as nonfamilial TN patients were analysed by RNA sequencing (RNA-seq) to confirm differential gene expression. Results: Seven probands were derived from 3 Chinese families. WES and Sanger sequencing identified MARS1 mutation c.2398C > A p.(Pro800Thr) in Family 1. MARS1 mutation was confirmed in 14/26 [53.8%] members of Family 1 in FTN family member group, while none of nonfamilial TN subjects had this MARS1 mutation. RNA-seq showed that 3 probands in Family 1 had higher expression of Fosl1 (Fos-like antigen 1) and NFE2 (Nuclear factor, erythroid 2) than 3 subjects in the nonfamilial TN subject group. Fosl1 and NFE2 are genes related to integrated stress response (ISR). Conclusion: MARS1 mutations may cause chronic activation of ISR, contribute to ISR pathophysiological changes in FTN, and cause/accelerate peripheral nerve degeneration. The findings of this study can enrich our knowledge of the role of molecular genetics in TN in humans. [ABSTRACT FROM AUTHOR]

Purpose: Depressive symptoms are among the most common afflictions that plague the parents of children with cancer. Given that resilience is an important factor that inhibits the development and progression of depressive symptoms, it can be harnessed as a potential solution for this psychological issue. This study aimed to identify the latent classes of resilience in the parents of children with cancer and explore the relationships of these classes with depressive symptoms. Methods: The study was conducted in three tertiary hospitals in mainland China. The parents of children (0–19 years old) with cancer were invited to participate in the study. We used standardised self-report questionnaires to collect data. A latent class analysis was performed to identify the classes of resilience in the parents of children with cancer. Results: The study recruited 258 participants. Four classes of resilience were identified, namely, good adaptation and optimism (24.9%), high strength and low control (12.1%), moderate resilience (37.3%), and maladaptation and low tenacity (25.6%). The mothers (OR = 3.48, P = 0.003) and parents with more than one child (OR = 2.73, P = 0.023) were more likely to belong to the maladaptation and low tenacity class. The depressive symptoms experienced by the participants differed significantly between the four classes of resilience (F = 30.80, P < 0.001). Conclusion: Targeted interventions should be developed and implemented based on the characteristics of each class of resilience to maximise the effectiveness of interventions and thereby promote parental well-being. [ABSTRACT FROM AUTHOR]

Highlights: A ginsenoside Rh2-based multifunctional liposome system (Rh2-lipo) was innovatively developed. Rh2-lipo not only innovatively challenges the position of cholesterol as a liposome component, but also provides another innovative potential system with multiple functions for anti-cancer drug delivery.Liposomes hold great potential in anti-cancer drug delivery and the targeting treatment of tumors. However, the clinical therapeutic efficacy of liposomes is still limited by the complexity of tumor microenvironment (TME) and the insufficient accumulation in tumor sites. Meanwhile, the application of cholesterol and polyethylene glycol (PEG), which are usually used to prolong the blood circulation and stabilize the structure of liposomes respectively, has been questioned due to various disadvantages. Herein, we developed a ginsenoside Rh2-based multifunctional liposome system (Rh2-lipo) to effectively address these challenges once for all. Different with the conventional ‘wooden’ liposomes, Rh2-lipo is a much more brilliant carrier with multiple functions. In Rh2-lipo, both cholesterol and PEG were substituted by Rh2, which works as membrane stabilizer, long-circulating stealther, active targeting ligand, and chemotherapy adjuvant at the same time. Firstly, Rh2 could keep the stability of liposomes and avoid the shortcomings caused by cholesterol. Secondly, Rh2-lipo showed a specifically prolonged circulation behavior in the blood. Thirdly, the accumulation of the liposomes in the tumor was significantly enhanced by the interaction of glucose transporter of tumor cells with Rh2. Fourth, Rh2-lipo could remodel the structure and reverse the immunosuppressive environment in TME. When tested in a 4T1 breast carcinoma xenograft model, the paclitaxel-loaded Rh2-lipo realized high efficient tumor growth suppression. Therefore, Rh2-lipo not only innovatively challenges the position of cholesterol as a liposome component, but also provides another innovative potential system with multiple functions for anti-cancer drug delivery. [ABSTRACT FROM AUTHOR]

Hand joints-based gesture recognition using a neural network provides excellent performance in hand gesture recognition. However, during the collection of sequential skeletal datasets, joints identification through hand pose estimations usually includes noise and even errors, which often diminish the accuracy of gesture recognition. To promote the availability of hand gesture recognition for such noisy datasets, this paper presents a nested interval unscented Kalman filter (UKF) with long short-term memory (NIUKF-LSTM) network to improve the accuracy of hand gesture recognition from noisy datasets. This nested interval method with the UKF changes the distribution of the sigma points based on two sampling intervals. By considering the information of previous frames, the nested interval method helps the NIUKF-LSTM network revise the noise in the sequential hand skeletal data and improve the recognition accuracy. The experimental results showing the removal of noisy skeletal data from the dynamic hand gesture dataset demonstrate the effectiveness of our NIUKF-LSTM network, which achieves better performance than do other state-of-the-art methods. [ABSTRACT FROM AUTHOR]