YOLOX-SwinT algorithm improves the accuracy of AO/OTA classification of intertrochanteric fractures by orthopedic trauma surgeons.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION: In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.

Overcoming Multidrug Resistance by Base-Editing-Induced Codon Mutation.

Multidrug resistance (MDR) is the main obstacle in cancer chemotherapy. ATP binding cassette (ABC) transporters on the MDR cell membrane can transport a wide range of antitumor drugs out of cells, which is one of the main causes of MDR. Therefore, disturbing ABC transporters becomes the key to reversing MDR. In this study, we implement a cytosine base editor (CBE) system to knock out the gene encoding ABC transporters by base editing. When the CBE system works in MDR cells, the MDR cells are manipulated, and the genes encoding ABC transporters can be inactivated by precisely changing single in-frame nucleotides to induce stop (iSTOP) codons. In this way, the expression of ABC efflux transporters is reduced and intracellular drug retention is significantly increased in MDR cells. Ultimately, the drug shows considerable cytotoxicity to the MDR cancer cells. Moreover, the substantial downregulation of P-glycoprotein (P-gp) and breast cancer resistance protein (BCRP) implies the successful application of the CBE system in the knockout of different ABC efflux transporters. The recovery of chemosensitivity of MDR cancer cells to the chemotherapeutic drugs revealed that the system has a satisfactory universality and applicability. We believe that the CBE system will provide valuable clues for the use of CRISPR technology to defeat the MDR of cancer cells.

Functional plasticity abnormalities over the lifespan of first-episode patients with major depressive disorder: a resting state fMRI study.

BACKGROUND: Neurodevelopmental and neurodegenerative theories of depression suggest that patients with major depressive disorder (MDD) may follow abnormal developmental, maturational, and aging processes. However, a lack of lifespan studies has precluded verification of these theories. Herein, we analyzed functional magnetic resonance imaging (fMRI) data to comprehensively characterize age-related functional trajectories, as measured by the fractional amplitude of low frequency fluctuations (fALFF), over the course of MDD. METHODS: In total, 235 MDD patients with age-differentiated onsets and 235 age- and sex-matched healthy controls (HC) were included in this study. We determined the pattern of age-related fALFF changes by cross-sectionally establishing the general linear model (GLM) between fALFF and age over a lifespan. Furthermore, the subjects were divided into four age groups to assess age-related neural changes in detail. Inter-group fALFF comparison (MDD vs. HC) was conducted in each age group and Granger causal analysis (GCA) was applied to investigate effective connectivity between regions. RESULTS: Compared with the HC, no significant quadratic or linear age effects were found in MDD over the entire lifespan, suggesting that depression affects the normal developmental, maturational, and degenerative process. Inter-group differences in fALFF values varied significantly at different ages of onset. This implies that MDD may impact brain functions in a highly dynamic way, with different patterns of alterations at different stages of life. Moreover, the GCA analysis results indicated that MDD followed a distinct pattern of effective connectivity relative to HC, and this may be the neural basis of MDD with age-differentiated onsets. CONCLUSIONS: Our findings provide evidence that normal developmental, maturational, and ageing processes were affected by MDD. Most strikingly, functional plasticity changes in MDD with different ages of onset involved dynamic interactions between neuropathological processes in a tract-specific manner.

Automatic identification of compressions and ventilations during CPR based on the fuzzy c-means clustering and deep belief network.

BACKGROUND: A transthoracic impedance (TTI) signal is an important indicator of the quality of chest compressions (CCs) during cardiopulmonary resuscitation (CPR). We proposed an automatic detection algorithm including the wavelet decomposition, fuzzy c-means (FCM) clustering, and deep belief network (DBN) to identify the compression and ventilation waveforms for evaluating the quality of CPR. METHODS: TTI signals were collected from a cardiac arrest model that electrically induced cardiac arrest in pigs. All signals were denoised using the wavelet and morphology method. The potential compression and ventilation waveforms were marked using an algorithm with a multi-resolution window. The compressions and ventilations in these waveforms were identified and classified using the FCM clustering and DBN methods. RESULTS: Using the FCM clustering method, the positive predictive values (PPVs) for compressions and ventilations were 99.7% and 95.7%, respectively. The sensitivities of recognition were 99.8% for compressions and 95.1% for ventilations. The DBN approach exhibited similar PPV and sensitivity results to the FCM clustering method. The time cost was satisfactory using either of these techniques. CONCLUSIONS: Our findings suggest that FCM clustering and DBN can be utilized to effectively and accurately evaluate CPR quality, and provide information for improving the success rate of CPR. Our real-time algorithms using FCM clustering and DBN eliminated most distortions and noises effectively, and correctly identified the compression and ventilation waveforms with a low time cost.

A telomerase-responsive nanoprobe with theranostic properties in tumor cells.

Multidrug resistance (MDR) is the main cause of treatment failure in clinical cancer chemotherapy due to the presence of P-glycoproteins (P-gp), which widely exist in stubborn drug-resistant tumor membranes and actively pump drugs from inside the tumor cell to the outside. In this study, we report a novel telomerase-responsive nanoprobe with theranostic properties for inhibiting P-gp expression and reversing MDR by gene silencing. This nanoprobe is composed of an AuNP assembled with telomerase primer, antisense oligonucleotide (ASO), and doxorubicin (Dox). When the designed nanoprobe is uptaken by the MDR cancer cells, the Dox and ASO are specifically released due to the extension of telomerase primer triggered by telomerase. The released ASO specifically hybridizes with multidrug resistance 1 (MDR1) mRNA sequence, which encodes the P-gp. As a result, the expression of P-gp is inhibited and the efflux of Dox is prevented with reduced MDR in cancerous cells. The results demonstrate that the nanoprobe based on telomerase switching for drug release and gene silencing, can both target cancer cells for delivering drugs and overcome the effect of efflux pumps. This work presents a novel paradigm for theranostics of MDR cancer and enhances the efficacy of chemotherapeutics.

A highly integrated DNA nanomachine operating in living cells powered by an endogenous stimulus.

Synthetic molecular machines have received increasing attention because of their great ability to mimic natural biological motors and create novel modes of motion. However, very few examples have been implemented with real autonomous movement inside living cells, due to the challenges of the driving force and highly integrated system design. In this work, we report an elegant, highly integrated DNA nanomachine that can be powered by endogenous ATP molecules and autonomously operated inside living cells without any auxiliary additives. It assembles all components on a single gold nanoparticle (AuNP) including a hairpin-locked swing arm encoding a start triggered by an intracellular target molecule and a two-stranded DNA track responding to the motion of the swing arm. When the intracellular target activates the nanomachine via the unlocking swing arm, the machine autonomously and progressively operates on the established DNA track via intramolecular toehold-mediated strand migration and internal ATP binding. This paper also demonstrates the machine's bioanalytical application for specific microRNA (miRNA) imaging in living cells.

Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples.

BACKGROUND: The use of speech based data in the classification of Parkinson disease (PD) has been shown to provide an effect, non-invasive mode of classification in recent years. Thus, there has been an increased interest in speech pattern analysis methods applicable to Parkinsonism for building predictive tele-diagnosis and tele-monitoring models. One of the obstacles in optimizing classifications is to reduce noise within the collected speech samples, thus ensuring better classification accuracy and stability. While the currently used methods are effect, the ability to invoke instance selection has been seldomly examined. METHODS: In this study, a PD classification algorithm was proposed and examined that combines a multi-edit-nearest-neighbor (MENN) algorithm and an ensemble learning algorithm. First, the MENN algorithm is applied for selecting optimal training speech samples iteratively, thereby obtaining samples with high separability. Next, an ensemble learning algorithm, random forest (RF) or decorrelated neural network ensembles (DNNE), is used to generate trained samples from the collected training samples. Lastly, the trained ensemble learning algorithms are applied to the test samples for PD classification. This proposed method was examined using a more recently deposited public datasets and compared against other currently used algorithms for validation. RESULTS: Experimental results showed that the proposed algorithm obtained the highest degree of improved classification accuracy (29.44%) compared with the other algorithm that was examined. Furthermore, the MENN algorithm alone was found to improve classification accuracy by as much as 45.72%. Moreover, the proposed algorithm was found to exhibit a higher stability, particularly when combining the MENN and RF algorithms. CONCLUSIONS: This study showed that the proposed method could improve PD classification when using speech data and can be applied to future studies seeking to improve PD classification methods.

[Design of a bowel sounds acquisition system based on C8051F340 MCU].

This paper introduces an eight channels dada acquisition system. A peripheral circuit based on C8051F340 MCU has been designed and its USB controller is used to transfer data, which realizes real-time detection, display and storage of bowel sounds. The system has many advantages such as high precision and stable performance, and thus provides a new means for the bowel sounds study.

[Epidemiological studies of sleep apnea-hypopnea syndrome: a questionnaire survey of Taiyuan].

OBJECTIVE: To obtain the epidemiological data of sleep apnea-hypopnea sydrome (SAHS) in Taiyuan. METHODS: A questionnaire survey was performed in 6 028 people living in Taiyuan. The prevalence of SAHS was estimated by a two-stage procedure. In the first stage, stratified cluster disproportional random sampling survey was performed in Taiyuan. 6 028 questionnaires were send to random sample of defined population in the 4 sites selected from 2 districts. The response rate was 85.11%. During the second stage 476 of those highly suspected of having SAHS (ESS >/= 9) underwent all-night polysomnographic (PSG) studies. RESULTS: From the study population, 179 were diagnosed as having SAHS. The overall prevalence was 3.5% (male 4.7% and female 1.9%). CONCLUSION: The prevalence of SAHS was 3.5% among Taiyuan.