Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.

Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need in clinical practice, physicians often rely on visual examination of specific areas, such as conjunctiva, to assess pallor. However, this method is subjective and relies on the physician's experience. Therefore, we proposed a deep learning prediction model based on three input images from different body parts, namely, conjunctiva, palm, and fingernail. By incorporating additional body part labels and employing a fusion attention mechanism, the model learns and enhances the salient features of each body part during training, enabling it to produce reliable results. Additionally, we employ a dual loss function that allows the regression model to benefit from well-established classification methods, thereby achieving stable handling of minority samples. We used a retrospective data set (EYES-DEFY-ANEMIA) to develop this model called Body-Part-Anemia Network (BPANet). The BPANet showed excellent performance in detecting anemia, with accuracy of 0.849 and an F1-score of 0.828. Our multi-body-part model has been validated on a prospectively collected data set of 101 patients in National Taiwan University Hospital. The prediction accuracy as well as F1-score can achieve as high as 0.716 and 0.788, respectively. To sum up, we have developed and validated a novel non-invasive hemoglobin prediction model based on image input from multiple body parts, with the potential of real-time use at home and in clinical settings.

Multimodal Assessment of Schizophrenia Symptom Severity From Linguistic, Acoustic and Visual Cues.

Assessing the condition of every schizophrenia patient correctly normally requires lengthy and frequent interviews with professionally trained doctors. To alleviate the time and manual burden on those mental health professionals, this paper proposes a multimodal assessment model that predicts the severity level of each symptom defined in Scale for the Assessment of Thought, Language, and Communication (TLC) and Positive and Negative Syndrome Scale (PANSS) based on the patient's linguistic, acoustic, and visual behavior. The proposed deep-learning model consists of a multimodal fusion framework and four unimodal transformer-based backbone networks. The second-stage pre-training is introduced to make each off-the-shelf pre-trained model learn the pattern of schizophrenia data more effectively. It learns to extract the desired features from the view of its modality. Next, the pre-trained parameters are frozen, and the light-weight trainable unimodal modules are inserted and fine-tuned to keep the number of parameters low while maintaining the superb performance simultaneously. Finally, the four adapted unimodal modules are fused into a final multimodal assessment model through the proposed multimodal fusion framework. For the purpose of validation, we train and evaluate the proposed model on schizophrenia patients recruited from National Taiwan University Hospital, whose performance achieves 0.534/0.685 in MAE/MSE, outperforming the related works in the literature. Through the experimental results and ablation studies, as well as the comparison with other related multimodal assessment works, our approach not only demonstrates the superiority of our performance but also the effectiveness of our approach to extract and integrate information from multiple modalities.

Mental Status Detection for Schizophrenia Patients via Deep Visual Perception.

Schizophrenia is a mental disorder that will progressively change a person's mental state and cause serious social problems. Symptoms of schizophrenia are highly correlated to emotional status, especially depression. We are thus motivated to design a mental status detection system for schizophrenia patients in order to provide an assessment tool for mental health professionals. Our system consists of two phases, including model learning and status detection. For the learning phase, we propose a multi-task learning framework to infer the patient's mental state, including emotion and depression severity. Unlike previous studies inferring emotional status mainly by facial analysis, in the learning phase, we adopted a Cross-Modality Graph Convolutional Network (CMGCN) to effectively integrate visual features from different modalities, including the face and context. We also designed task-aware objective functions to realize better model convergence for multi-task learning, i.e., emotion recognition and depression estimation. Further, we followed the correlation between depression and emotion to design the Emotion Passer module, to transfer the prior knowledge on emotion to the depression model. For the detection phase, we drew on characteristics of schizophrenia to detect the mental status. In the experiments, we performed a series of experiments on several benchmark datasets, and the results show that the proposed learning framework boosts state-of-the-art (SOTA) methods significantly. In addition, we take a trial on schizophrenia patients, and our system can achieve 69.52 in mAP in a real situation.

[Construction of expression vector of ALAg and immune protection of its recombinant protein induced in mice].

OBJECTIVE: To determine the candidate genes for engineering vaccine of Ascaris lumbricoides. METHODS: pMD18-T-ALAg and plasmid expression vector pET-28a(+) were digested with BamH I and EcoR I and linked to each other. The resultant plasmid pET-28a(+)-ALAg was transferred into E. coli BL21 (DE3) and its expression was induced with IPTG, and the recombinant ALAg(rALAg) was purified. A total of 30 mice were equally divided into 3 groups, the mice in each group were injected with rALAg-FCA, FCA and PBS respectively, then they were attacked by infectious eggs of Ascaris (3 600 per mouse). The IgG levels in sera of mice in each group were detected by indirect ELASA. RESULTS: rALAg was recognized by the sera from repeatedly Ascaris lumbricoides inoculated rabbits. The numbers of larvae of Ascaris lumbricoides from liver and lung of mice were 25.30 +/- 4.55 in the rALAg-FCA group and 57.60 +/- 5.76 in the PBS group, respectively, the former being the reducing rate of 69.26%, and the difference among the 3 groups showed statistical significances (P < 0.01). The IgG levels (A450 value) of the rALAg-FCA, FCA and PBS groups were 0.858 +/- 0.003, 0.149 +/- 0.004 and 0.134 +/- 0.004, respectively, there were statistical differences among them (P < 0.01). CONCLUSION: ALAg can be used as a candidate gene of genetic engineering vaccine of Ascaris lumbricoides.