Automatic detection of facial expressions during the Cyberball paradigm in Borderline Personality Disorder: a pilot study.

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios.

Dissociation of Arousal Index Between REM and NREM Sleep in Elderly Adults with Cognitive Impairment, No Dementia: A Pilot Study.

BACKGROUND: Sleep disruption in elderly has been associated with an increased risk of cognitive impairment and its transition into Alzheimer's disease (AD). High arousal indices (AIs) during sleep may serve as an early-stage biomarker of cognitive impairment non-dementia (CIND). OBJECTIVE: Using full-night polysomnography (PSG), we investigated whether CIND is related to different AIs between NREM and REM sleep stages. METHODS: Fourteen older adults voluntarily participated in this population-based study that included Mini-Mental State Examination, Neuropsi battery, Katz Index of Independence in Activities of Daily Living, and single-night PSG. Subjects were divided into two groups (n = 7 each) according to their results in Neuropsi memory and attention subtests: cognitively unimpaired (CU), with normal results; and CIND, with -2.5 standard deviations in memory and/or attention subtests. AIs per hour of sleep during N1, N2, N3, and REM stages were obtained and correlated with Neuropsi total score (NTS). RESULTS: AI (REM)  was significantly higher in CU group than in CIND group. For the total sample, a positive correlation between AI (REM)  and NTS was found (r = 0.68, p = 0.006), which remained significant when controlling for the effect of age and education. In CIND group, the AI (N2)  was significantly higher than the AI (REM) . CONCLUSION: In CIND older adults, this attenuation of normal arousal mechanisms in REM sleep are dissociated from the relative excess of arousals observed in stage N2. We propose as probable etiology an early hypoactivity at the locus coeruleus noradrenergic system, associated to its early pathological damage, present in the AD continuum.

Transgenic rodents as dynamic models for the study of respiratory rhythm generation and modulation: a scoping review and a bibliometric analysis.

The pre-Bötzinger complex, situated in the ventrolateral medulla, serves as the central generator for the inspiratory phase of the respiratory rhythm. Evidence strongly supports its pivotal role in generating, and, in conjunction with the post-inspiratory complex and the lateral parafacial nucleus, in shaping the respiratory rhythm. While there remains an ongoing debate concerning the mechanisms underlying these nuclei's ability to generate and modulate breathing, transgenic rodent models have significantly contributed to our understanding of these processes. However, there is a significant knowledge gap regarding the spectrum of transgenic rodent lines developed for studying respiratory rhythm, and the methodologies employed in these models. In this study, we conducted a scoping review to identify commonly used transgenic rodent lines and techniques for studying respiratory rhythm generation and modulation. Following PRISMA guidelines, we identified relevant papers in PubMed and EBSCO on 29 March 2023, and transgenic lines in Mouse Genome Informatics and the International Mouse Phenotyping Consortium. With strict inclusion and exclusion criteria, we identified 80 publications spanning 1997-2022 using 107 rodent lines. Our findings revealed 30 lines focusing on rhythm generation, 61 on modulation, and 16 on both. The primary in vivo method was whole-body plethysmography. The main in vitro method was hypoglossal/phrenic nerve recordings using the en bloc preparation. Additionally, we identified 119 transgenic lines with the potential for investigating the intricate mechanisms underlying respiratory rhythm. Through this review, we provide insights needed to design more effective experiments with transgenic animals to unravel the mechanisms governing respiratory rhythm. The identified transgenic rodent lines and methodological approaches compile current knowledge and guide future research towards filling knowledge gaps in respiratory rhythm generation and modulation.

Liver cancer in Hidalgo State, Mexico: analysis of the status, risk factors and regional public health policy requirements: a cross-sectional correlational study

ABSTRACT BACKGROUND: In Latin America, liver cancer is one of the top causes of cancer mortality. It is the fifth most common cause of death among malignant tumors in Mexico and is the leading cause in Hidalgo State (43.8% of the population living in poverty). OBJECTIVE: To conduct a correlational analysis on the main risk factors for liver cancer in Hidalgo State, Mexico, including municipal disaggregation and comparison with the national level. DESIGN AND SETTING: Cross-sectional, correlational, descriptive and comparative epidemiological study using Mexican governmental databases covering 1990-2019. METHODS: A comprehensive review of the databases of the General Directorate of Health Information (DGIS) was performed to analyze official death figures, hospital discharges and national and municipal population projections, using specific search criteria defined in the Global Burden of Disease classification, based on the risk factors for liver cancer. RESULTS: Liver cancer rates showed an evident rise in Hidalgo (183%), moving from 21st place in Mexico in 1990 to 9th place in 2019. This increase was correlated with alcoholism. An increasing trend for liver cancer deaths, of 133.89%, is projected for 2030. Females and the population over 60 years of age are more affected. There are some critical regions with liver cancer death rates twice the national rate or more. CONCLUSION: Targeted effective public health strategies should be structured by identifying, characterizing and regionalizing critical marginalized municipalities that are vulnerable to alcoholism and other risk factors for liver cancer. This approach may be helpful for other states in Mexico or similar countries.

Liver cancer in Hidalgo State, Mexico: analysis of the status, risk factors and regional public health policy requirements: a cross-sectional correlational study.

BACKGROUND: In Latin America, liver cancer is one of the top causes of cancer mortality. It is the fifth most common cause of death among malignant tumors in Mexico and is the leading cause in Hidalgo State (43.8% of the population living in poverty). OBJECTIVE: To conduct a correlational analysis on the main risk factors for liver cancer in Hidalgo State, Mexico, including municipal disaggregation and comparison with the national level. DESIGN AND SETTING: Cross-sectional, correlational, descriptive and comparative epidemiological study using Mexican governmental databases covering 1990-2019. METHODS: A comprehensive review of the databases of the General Directorate of Health Information (DGIS) was performed to analyze official death figures, hospital discharges and national and municipal population projections, using specific search criteria defined in the Global Burden of Disease classification, based on the risk factors for liver cancer. RESULTS: Liver cancer rates showed an evident rise in Hidalgo (183%), moving from 21st place in Mexico in 1990 to 9th place in 2019. This increase was correlated with alcoholism. An increasing trend for liver cancer deaths, of 133.89%, is projected for 2030. Females and the population over 60 years of age are more affected. There are some critical regions with liver cancer death rates twice the national rate or more. CONCLUSION: Targeted effective public health strategies should be structured by identifying, characterizing and regionalizing critical marginalized municipalities that are vulnerable to alcoholism and other risk factors for liver cancer. This approach may be helpful for other states in Mexico or similar countries.

Chronic Undernutrition Differentially Changes Muscle Fiber Types Organization and Distribution in the EDL Muscle Fascicles.

Fiber type composition, organization, and distribution are key elements in muscle functioning. These properties can be modified by intrinsic and/or extrinsic factors, such as undernutrition and injuries. Currently, there is no methodology to quantitatively analyze such modifications. On one hand, we propose a fractal approach to determine fiber type organization, using the fractal correlation method in software Fractalyse. On the other hand, we applied the kernel methodology from machine learning to build radial-basis functions for the spatial distribution of fibers (distribution functions), by dividing into square cells a two-dimensional binary image for the spatial distribution of fibers from a muscle fascicle and mounting on each cell a radial-basis function in such a way that the sum of all cell functions creates a smooth version of the fiber histogram on the cell grid. The distribution functions thus created belong in a reproducing kernel Hilbert space which permits us to regard them as vectors and measure distances and angles between them. In the present study, we analyze fiber type organization and distribution in fascicles (F2, F3, F4, and F5) of the extensor digitorum longus muscle (EDLm) from control and undernourished male rats. Fibers were classified according to the ATPase activity in slow, intermediate, and fast. Then, (x, y) coordinates of fibers were used to build binary images and distribution functions for each fiber type and both conditions. The fractal organization analysis showed that fast and intermediate fibers, from both groups, had a fractal organization within the four fascicles, i.e., the fiber assembly is distributed in clusters. We also show that chronic undernutrition altered the organization of fast fibers in the F3, although it still is considered a fractal organization. Distribution function analysis showed that each fiber type (slow, intermediate, and fast) has a unique distribution within the fascicles, in both conditions. However, chronic undernutrition modified the intra-fascicular fiber type distributions, except in the F2. Altogether, these results showed that the methodology herein proposed allows for analyzing fiber type organization and distribution modifications. On the other side, we show that chronic undernutrition alters not only the fiber type composition but also the organization and distribution, which could affect the muscle functioning, and ultimately, its behavior (e.g., locomotion).

Characterization and Classification of Electrophysiological Signals Represented as Visibility Graphs Using the Maxclique Graph.

Detection, characterization and classification of patterns within time series from electrophysiological signals have been a challenge for neuroscientists due to their complexity and variability. Here, we aimed to use graph theory to characterize and classify waveforms within biological signals using maxcliques as a feature for a deep learning method. We implemented a compact and easy to visualize algorithm and interface in Python. This software uses time series as input. We applied the maxclique graph operator in order to obtain further graph parameters. We extracted features of the time series by processing all graph parameters through K-means, one of the simplest unsupervised machine learning algorithms. As proof of principle, we analyzed integrated electrical activity of XII nerve to identify waveforms. Our results show that the use of maxcliques allows identification of two distinct types of waveforms that match expert classification. We propose that our method can be a useful tool to characterize and classify other electrophysiological signals in a short time and objectively. Reducing the classification time improves efficiency for further analysis in order to compare between treatments or conditions, e.g., pharmacological trials, injuries, or neurodegenerative diseases.

Differential effect of chronic undernutrition on the fiber type composition of fascicles in the extensor digitorum longus muscles of the rat.

Several studies have shown that chronic low food consumption alters the composition and metabolism of the extensor digitorum longus muscle (EDLm) fiber types. EDLm is constituted by four independent fascicles (F2-F5) of different sizes; their constitution and metabolism, however, as well as how chronic undernourishment affects these is virtually unknown. Thus, the aim of this study is to evaluate the relative fiber type composition and metabolism of each independent fascicle in the EDLm, using control and chronically undernourished young male rats by using the alkaline ATPase and NADH-TR histochemical techniques. Our results indicate that all control fascicles showed a higher percentage of intermediate fibers (P<0.001), except for F5, which had a higher percentage of fast fibers (P<0.001). After chronic undernutrition, the proportion of intermediate fibers decreased in F4 (P<0.05) and increased in F5 (P<0.001), whereas fast fibers decreased in F3 (P<0.05) and F5 (P<0.001). When we investigated fiber metabolism we found that F3 and F4 had a similar composition (mainly glycolytic), whereas F2 and F5 predominantly contained oxidative fibers. All fascicles of chronic undernourished rats showed a general decrease in oxidative fibers (P>0.05), except for F3, in which oxidative fibers increased (P<0.05). After determining the possible predominant metabolism expressed in intermediate fibers, we propose that chronic undernutrition induces the transformation of fast-glycolytic to intermediate-oxidative/glycolytic fibers, mainly in F3 and F5. Our observations confirm that chronic undernourishment differentially affects the fiber types of each fascicle in the EDLm, which could alter their individual physiological contractile properties.