[Objective] To explore how to use music therapy to treat insomnia. [Methods] Through the analysis of the characteristics of traditional Chinese medicine theory, reflect on the pattern of the TCM therapy for insomnia, excavate the tune theory in Neijing, with unique angle of view to explain music therapy, combined with traditional Chinese medicine five psychological states of disorder in the concrete implementation plan of the music therapy. [Re-sults] Chinese medicine five kinds of psychological disorder of music therapy treating insomnia provides a good way of thinking, different psychological dis-order states corresponding to different melodies of music therapy, the system in the treatment of insomnia. [Conclusion] The emerging treatment of music therapy is to be carried forward, as a kind of special treatment, has been accepted by more and more people, the five kinds of TCM psychological disorder states in detail dividing the specific psychological insomnia, has played a guiding role to the design of the music prescription.

Three-parent test tube baby technology is important to solve the mitochondrial genetic disease.Once available,it raises greatly ethical controversy such as breaking traditional family values,hitting the religious belief,existing unknown risks,correctly handling the failed embryo,as well as the influence on the social status of the babies.Regarding these controversy,we can discuss it from several aspects.Because the development of ethics is behind the progress of science and technology,we should affirm the value of three-parent test tube baby technology and balance the development of science and technology with respecting the religious beliefs.Strict supervision system and standard application system reflect our respect for life.Incomprehension to the unknown things should become the motivation of our inquiry.We should face up to our fear of three-parent test tube baby technology,and thus to strengthen research and deepen understanding.Based on the above argument,this paper puts forward the ethical principles that should be followed in the development of three-parent test tube baby technology,namely respect,benefit,no harm and justice.

Since CRISPR/Cas9 has been discovered,it opens a new era for gene therapy with its low cast,high efficiency,short cycle,easy operation and other characteristics.In 2015,Huang Junjiu had used the technique to nodify human embryonic cell for the first time,leading to widespread attention at home and abroad.For nearly 50 years,ethical discussion of gene therapy has never stopped.Through reviewing the development of gene therapy,this paper analyzed several key issues of ethical controversy in gene therapy,such as safety,effectiveness,justice,right,interest orientation and so on,and put forward that gene therapy and ethics are not two opposite sides and cannot be treated separately,the development of gene therapy should follow certain ethical norms and technical norms and it should use the bioethical principles to direct gene therapy.

Purpose: Male overweight and obesity could affect sperm quality and reproductive health. However, the impact of body mass index (BMI) on assisted reproductive technology (ART) outcomes in oligospermia and/or asthenospermia patients is yet lacking. This study aims to assess the impact of paternal BMI on ART and neonatal outcomes among oligozoospermia and/or asthenospermia patients undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI). Materials and Methods: In this study, 2,075 couples undergoing their first fresh embryo transfer between January 2015 and June 2022 were recruited. Following the World Health Organization’s (WHO’s) categories, couples were stratified into three cohorts based on paternal BMI: normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2). Modified Poisson regression models were used to assess the associations of paternal BMI with fertilization, in vitro embryonic development, and pregnancy outcomes. Logistic regression models were performed to investigate the associations of paternal BMI with pregnancy loss and neonatal outcomes. Furthermore, stratified analyses were performed based on fertilization methods, male infertility cause, and maternal BMI. Results: Higher paternal BMI is associated with a lower likelihood of achieving normal fertilized (p-trend=0.002), Day 3 transferable (p-trend=0.007), and high-quality embryos (p-trend=0.046) in IVF cycles, rather than in ICSI cycles. Paternal BMI of oligospermia or asthenospermia was negatively correlated with day 3 transferable (p-trend=0.013 and 0.030) and high-quality embryos (p-trend=0.024 and 0.027). Moreover, for neonatal outcomes, paternal BMI was positively associated with macrosomia (p-trend=0.019), large for gestational age (LGA) (p-trend=0.031), and very LGA (p-trend=0.045). Conclusions Our data suggested that higher paternal BMI was associated with fetal overgrowth, reduced fertilization, and embryonic development potential. Among males with oligospermia and/or asthenospermia, the impact of overweight and obesity on the choice of fertilization method and the long-term effects on their offspring need to be further investigated.

Objective To investigate the correlation between the expression of Hsa_circ_0026352 and the clinical characteristics of breast cancer(BC) patients, to evaluate the value of Hsa_circ_0026352 as a diagnostic marker of breast cancer. Methods Human circRNA microarray was used to screen the different expression of circRNAs in BC tissues. qRT-PCR was used to verify the expression of Hsa_circ_0026352 in BC tissue and peripheral blood. CircRNA structure were performed by circPrimer1.2 software. T-test, ANOVA analysis, curve regression analysis and ROC curve analysis were performed to determine the diagnostic values of Hsa_circ_0026352. Results Hsa_circ_0026352 was significantly down-regulated in both breast cancer tissues and peripheral blood (P < 0.01). The AUC of Hsa_circ_0026352 were 0.881 in tissues and 0.826 in peripheral blood (both P < 0.01). The relative expression of Hsa_circ_0026352 in peripheral blood of BC cancer patients was negatively correlated with CA153 level (P < 0.05). The AUC of Hsa_circ_0026352 in peripheral blood of patients with ER positive, early stage breast cancer and breast neoplasm metastasis were 0.710, 0.771 and 0.722 (all P < 0.01), respectively. Conclusion Hsa_circ_0026352 could be a novel predictive biomarker for diagnosis of BC.

Transcriptional phenotypic drug discovery has achieved great success,and various com-pound perturbation-based data resources,such as connectivity map(CMap)and library of inte-grated network-based cellular signatures(LINCS),have been presented.Computational strategies fully mining these resources for phenotypic drug discovery have been proposed.Among them,the fundamental issue is to define the proper similarity between transcriptional profiles.Tra-ditionally,such similarity has been defined in an unsupervised way.However,due to the high dimensionality and the existence of high noise in high-throughput data,similarity defined in the tra-ditional way lacks robustness and has limited performance.To this end,we present DrSim,which is a learning-based framework that automatically infers similarity rather than defining it.We evalu-ated DrSim on publicly available in vitro and in vivo datasets in drug annotation and repositioning.The results indicated that DrSim outperforms the existing methods.In conclusion,by learning tran-scriptional similarity,DrSim facilitates the broad utility of high-throughput transcriptional pertur-bation data for phenotypic drug discovery.The source code and manual of DrSim are available at https://github.com/bm2-lab/DrSim/.

Objective:To establish a predictive model nomogram for 30-day death in patients with sepsis-associated acute kidney injury (SA-AKI) by using the data from the large international database, the Electronic Intensive Care Unit-Collaborative Research Database (eICU-CRD), and to validate its predictive performance.Methods:A retrospective cohort study was conducted using data from the eICU-CRD. Data of SA-AKI patients were screened from the eICU-CRD database, including demographic characteristics, medical history, SA-AKI type, Kidney Disease: Improving Global Outcomes (KDIGO)-AKI staging, severity of illness scores, vital signs, laboratory indicators, and treatment measures; with admission time as the observation start point, death as the outcome event, and a follow-up time of 30 days. Relevant variables of patients with different 30-day prognoses were compared. Univariate Logistic regression analysis and multivariate Logistic regression forward likelihood ratio analysis were used to screen for risk factors associated with 30-day death in SA-AKI patients, and a predictive model nomogram was constructed. Receiver operator characteristic curve (ROC curve), calibration curve, and Hosmer-Lemeshow test were used to validate the predictive performance of the model.Results:A total of 201 SA-AKI patients' data were finally enrolled, among which 51 survived for 30 days and 150 died, with a mortality of 74.63%. Compared with the survival group, patients in the death group were older [years old: 68 (60, 78) vs. 59 (52, 69), P < 0.01], had lower body weight, proportion of transient SA-AKI, platelet count (PLT) and blood glucose [body weight (kg): 79 (65, 95) vs. 91 (71, 127), proportion of transient SA-AKI: 61.33% (92/150) vs. 82.35% (42/51), PLT (×10 9/L): 207 (116, 313) vs. 260 (176, 338), blood glucose (mmol/L): 5.5 (4.4, 7.1) vs. 6.4 (5.1, 7.6), all P < 0.05] and higher proportion of persistent SA-AKI, sequential organ failure assessment (SOFA) score, lactic acid (Lac), and total bilirubin [TBil; proportion of persistent SA-AKI: 38.67% (58/150) vs. 17.65% (9/51), SOFA score: 7 (5, 22) vs. 5 (2, 7), Lac (mmol/L): 0.4 (0.2, 0.7) vs. 0.3 (0.2, 0.4), TBil (μmol/L): 41.0 (17.1, 51.3) vs. 18.8 (17.1, 34.2), all P < 0.05]. Univariate Logistic regression analysis showed that age [odds ratio ( OR) = 1.035, 95% confidence interval (95% CI) was 1.013-1.058, P = 0.002], body weight ( OR = 0.987, 95% CI was 0.977-0.996, P = 0.007), persistent SA-AKI ( OR = 2.942, 95% CI was 1.333-6.491, P = 0.008), SOFA score ( OR = 1.073, 95% CI was 1.020-1.129, P = 0.006), PLT ( OR = 0.998, 95% CI was 0.996-1.000, P = 0.034), Lac ( OR = 1.142, 95% CI was 1.009-1.292, P = 0.035), TBil ( OR = 1.422, 95% CI was 1.070-1.890, P = 0.015) were associated with 30-day death risk in SA-AKI patients. Multivariate Logistic regression forward likelihood ratio analysis showed that age ( OR = 1.051, 95% CI was 1.023-1.079, P = 0.000), body weight ( OR = 0.985, 95% CI was 0.974-0.995, P = 0.005), cardiovascular disease ( OR = 9.055, 95% CI was 1.037-79.084, P = 0.046), persistent SA-AKI ( OR = 3.020, 95% CI was 1.258-7.249, P = 0.013), SOFA score ( OR = 1.076, 95% CI was 1.013-1.143, P = 0.017), and PLT ( OR = 0.997, 95% CI was 0.995-1.000, P = 0.030) were independent risk factors for 30-day death in SA-AKI patients. Based on the above risk factors, a predictive model nomogram for 30-day death in SA-AKI patients was constructed. ROC curve analysis showed that the area under the ROC curve (AUC) of the model was 0.798 (95% CI was 0.722-0.873), with a sensitivity of 86.7% and a specificity of 62.7%. Calibration curve showed that the fitted curve was close to the standard line, indicating that the predicted probability was close to the actual probability, suggesting good predictive performance of the model. Hosmer-Lemeshow test showed χ 2 = 6.393, df = 8, P = 0.603 > 0.05, suggesting that the model could fit the observed data well. The quality of model fitting was judged by the accuracy of model prediction. The results showed that the prediction accuracy rate of the model was 95.3%, and the overall prediction accuracy rate of the model was 81.6%, indicating good model fitting. Conclusion:A predictive model for 30-day death in SA-AKI patients based on risk factors can be successfully constructed, and the model has high accuracy, sensitivity, reliability, and certain specificity, which can help to early identify high-risk patients for death and adopt more proactive treatment strategies.

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

Hearing loss has become increasingly prevalent and causes considerable disability, thus gravely burdening the global economy. Irreversible loss of hair cells is a main cause of sensorineural hearing loss, and currently, the only relatively effective clinical treatments are limited to digital hearing equipment like cochlear implants and hearing aids, but these are of limited benefit in patients. It is therefore urgent to understand the mechanisms of damage repair in order to develop new neuroprotective strategies. At present, how to promote the regeneration of functional hair cells is a key scientific question in the field of hearing research. Multiple signaling pathways and transcriptional factors trigger the activation of hair cell progenitors and ensure the maturation of newborn hair cells, and in this article, we first review the principal mechanisms underlying hair cell reproduction. We then further discuss therapeutic strategies involving the co-regulation of multiple signaling pathways in order to induce effective functional hair cell regeneration after degeneration, and we summarize current achievements in hair cell regeneration. Lastly, we discuss potential future approaches, such as small molecule drugs and gene therapy, which might be applied for regenerating functional hair cells in the clinic.
Infant, Newborn ; Humans ; Hair Cells, Auditory, Inner/physiology* ; Ear, Inner/physiology* ; Hair Cells, Auditory/physiology* ; Regeneration/genetics* ; Stem Cells