Identification of Potential Ferroptosis Biomarkers and Analysis of Immune Cell Infiltration in Psoriasis Using Machine Learning.

Background: Ferroptosis is a type of cell death characterized by the accumulation of iron-dependent lethal lipid peroxides, which is associated with various pathophysiological processes. Psoriasis is a chronic autoimmune skin disease accompanied by abnormal immune cell infiltration and excessive production of lipid reactive oxygen species (ROS). Currently, its pathogenesis remains elusive, especially the potential role of ferroptosis in its pathophysiological process. Methods: The microarrays GSE13355 (58 psoriatic skin specimens versus 122 healthy skin specimens) and the ferroptosis database were employed to identify the common differentially expressed genes (DEGs) associated with psoriasis and ferroptosis. The functions of common DEGs were investigated through functional enrichment analysis and protein-protein interaction analysis. The potential diagnostic markers for psoriasis among the common DEGs were identified using four machine-learning algorithms. DGIdb was utilized to explore potential therapeutic agents for psoriasis. Additionally, CIBERSORT was employed to investigate immune infiltration in psoriasis. Results: A total of 8 common DEGs associated with psoriasis and ferroptosis were identified, which are involved in intercellular signaling and affect pathways of cell response to stress and stimulation. Four machine-learning algorithms were employed to identify poly (ADP-ribose) polymerase 12 (PARP12), frizzled homolog 7 (FZD7), and arachidonate 15-lipoxygenase (ALOX15B) among the eight common DEGs as potential diagnostic markers for psoriasis. A total of 18 drugs targeting the five common DEGs were identified as potential candidates for treating psoriasis. Additionally, significant changes were observed in the immune microenvironment of patients with psoriasis. Conclusion: This study has contributed to our enhanced comprehension of ferroptosis-related genes as potential biomarkers for psoriasis diagnosis, as well as the alterations in the immune microenvironment associated with psoriasis. Our findings offer valuable insights into the diagnosis and treatment of psoriasis.

Adult vasovagal syncope with abdominal pain diagnosed by head-up tilt combined with transcranial doppler: a preliminary study.

BACKGROUND: Syncope is a common condition that increases the risk of injury and reduces the quality of life. Abdominal pain as a precursor to vasovagal syncope (VVS) in adults is rarely reported and is often misdiagnosed.​. METHODS: We present three adult patients with VVS and presyncopal abdominal pain diagnosed by synchronous multimodal detection (transcranial Doppler [TCD] with head-up tilt [HUT]) and discuss the relevant literature. RESULTS: Case 1: A 52-year-old man presented with recurrent decreased consciousness preceded by six months of abdominal pain. Physical examinations were unremarkable. Dynamic electrocardiography, echocardiography, head and neck computed tomography angiography, magnetic resonance imaging (MRI), and video electroencephalogram showed no abnormalities. Case 2: A 57-year-old woman presented with recurrent syncope for 30 + years, accompanied by abdominal pain. Physical examination, electroencephalography, and MRI showed no abnormalities. Echocardiography showed large right-to-left shunts. Case 3: A 30-year-old woman presented with recurrent syncope for 10 + years, with abdominal pain as a precursor. Physical examination, laboratory analysis, head computed tomography, electrocardiography, and echocardiography showed no abnormalities. Syncope secondary to abdominal pain was reproduced during HUT. Further, HUT revealed vasovagal syncope, and synchronous TCD showed decreased cerebral blood flow; the final diagnosis was VVS in all cases. CONCLUSIONS: Abdominal pain may be a precursor of VVS in adults, and our findings enrich the clinical phenotypic spectrum of VVS. Prompt recognition of syncopal precursors is important to prevent incidents and assist in treatment decision-making. Abdominal pain in VVS may be a sign of sympathetic overdrive. Synchronous multimodal detection can help in diagnosing VVS and understanding hemodynamic mechanisms.

Federated Domain Adaptation via Transformer for Multi-Site Alzheimer's Disease Diagnosis.

In multi-site studies of Alzheimer's disease (AD), the difference of data in multi-site datasets leads to the degraded performance of models in the target sites. The traditional domain adaptation method requires sharing data from both source and target domains, which will lead to data privacy issue. To solve it, federated learning is adopted as it can allow models to be trained with multi-site data in a privacy-protected manner. In this paper, we propose a multi-site federated domain adaptation framework via Transformer (FedDAvT), which not only protects data privacy, but also eliminates data heterogeneity. The Transformer network is used as the backbone network to extract the correlation between the multi-template region of interest features, which can capture the brain abundant information. The self-attention maps in the source and target domains are aligned by applying mean squared error for subdomain adaptation. Finally, we evaluate our method on the multi-site databases based on three AD datasets. The experimental results show that the proposed FedDAvT is quite effective, achieving accuracy rates of 88.75%, 69.51%, and 69.88% on the AD vs. NC, MCI vs. NC, and AD vs. MCI two-way classification tasks, respectively.

Gene-related Parkinson's disease diagnosis via feature-based multi-branch octave convolution network.

Parkinson's disease (PD) is a common neurodegenerative disease in the elderly population. PD is irreversible and its diagnosis mainly relies on clinical symptoms. Hence, its effective diagnosis is vital. PD has the related gene mutation called gene-related PD, which can be diagnosed not only in the specific PD patients, but also in the healthiest people without clinical symptoms of PD. Since mutations in PD-related genes can affect healthy people, and unaffected PD-related gene carriers can develop into PD patients, it is very necessary to distinguish gene-related PD diseases. The magnetic resonance imaging (MRI) has a lot of information about brain tissue, which can distinguish gene-related PD diseases. However, the limited amount of the gene-related cohort in PD is a challenge for further diagnosis. Therefore, we develop a joint learning framework called feature-based multi-branch octave convolution network (FMOCNN), which uses MRI data for gene-related cohort PD diagnosis. FMOCNN performs sample-feature selection to learn discriminative samples and features and contains a deep neural network to obtain high-level feature representation from various feature types. Specifically, we first train a cardinality constrained sample-feature selection (CCSFS) model to select informative samples and features. We then establish a multi-branch octave convolution neural network (MBOCNN) to jointly train multiple feature inputs. High/low-frequency learning in MBOCNN is exploited to reduce redundant feature information and enhance the feature expression ability. Our method is validated on the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset. Experiments demonstrate that our method achieves promising classification performance and outperforms similar algorithms.

Effective management of patients with acute ischemic stroke based on lean production on thrombolytic flow optimization.

The efficacy of thrombolytic therapy for acute ischemic stroke (AIS) decreases when the administration of tissue plasminogen activator (tPA) is delayed. Derived from Toyota Production System, lean production aims to create top-quality products with high-efficiency procedures, a concept that easily applies to emergency medicine. In this study, we aimed to determine whether applying lean principles to flow optimization could hasten the initiation of thrombolysis. A multidisciplinary team (Stroke Team) was organized to implement an ongoing, continuous loop of lean production that contained the following steps: decomposition, recognition, intervention, reengineering and assessment. The door-to-needle time (DNT) and the percentage of patients with DNT ≤ 60 min before and after the adoption of lean principles were used to evaluate the efficiency of our flow optimization. Thirteen patients with AIS in the pre-lean period and 43 patients with AIS in the lean period (23 in lean period I and 20 patients in lean period II) were consecutively enrolled in our study. After flow optimization, we reduced DNT from 90 to 47 min (p < 0.001¤). In addition, the percentage of patients treated ≤60 min after hospital arrival increased from 38.46 to 75.0 % (p = 0.015¤). Adjusted analysis of covariance confirmed a significant influence of optimization on delay of tPA administration (p < 0.001). The patients were more likely to have a good prognosis (mRS ≤ 2 at 90 days) after the flow optimization (30.77-75.00 %, p = 0.012¤). Our study may offer an effective approach for optimizing the thrombolytic flow in the management of AIS.

Diabetes alters the expression of partial vasoactivators in cerebral vascular disease susceptible regions of the diabetic rat.

BACKGROUND: The pathogenesis between cerebral vascular disease (CVD) and the endothelial dysfunction (ETD) remains elusive in diabetes. Therefore, we investigated the expression of partial vasoactivators which be closely relative to ETD in CVD susceptible brain regions in the diabetic rat. The aim was to search some possible pathogenesis. METHODS: Diabetes was induced by a single intraperitoneal injection of streptozotocin and a high lipid/sugar diet. The expression of vasoactivators ET-1, CGRP, VCAM-1, ICAM-1 and P-selectin were assessed by immunohistochemical staining and measurement of optic density of positive cells in the frontal and temporal lobe, basal ganglia and thalamus at 4 weeks after establishment of the diabetic model. RESULTS: The expression of ET-1, VCAM-1, ICAM-1 and P-selectin significantly increased and CGRP significantly decreased in the diabetic group, and the expression of these vasoactivators was significantly different among the frontal, temporal lobe, basal ganglia and thalamus, and among the emotion, splanchno-motor and neuroendocrine center in the diabetic group. CONCLUSIONS: Diabetes alters the expression of partial vasoactivators in cerebral vascular disease susceptible regions of the diabetic rat. Therefore, we suggested that CVD complications in diabetes are partly caused by ETD via an imbalance expression of endothelial vasoactivators, which might be associated with dysfunction of emotion, autonomic nerve and endocrine center. However, further studies are warranted.