Biology subjects

Ali, S. H.

Publications and source records attributed to Ali, S. H..

4 recordsLinked to original sources

bioRxiv2026

The influence of force on the encoding and perception of affective touch

Stroking touch has been shown to be most pleasant at intermediate velocities of 1-10 cm.s-1, which relates well to the activity of C-low threshold mechanoreceptors (C-LTMRs), also called C-tactile afferents in humans, that have been implicated in encoding positive affective touch. This well-established finding has been demonstrated at gentle stroking forces (typically 0.4 N peak normal force), yet few studies have investigated the effect of force on the perception of stroking touch or on the activity of C-LTMRs. Presently, we investigated the pleasantness and intensity of stroking touch (0.3, 1, 3, 10, 30 cm.s-1) at different forces (0.2, 0.4, 0.8, 1.2, 1.6, 2.0 N) on hairy forearm skin and compared this to responses from C-LTMRs during stroking touch at the same velocities, but over fewer forces (0.05, 0.4, 1.5 N). We found significant effects of stroking velocity and force for both tactile pleasantness and intensity ratings. Pleasantness showed the typical inverted-U shaped relationship over stroking velocities, but this was modified by force: higher forces significantly decreased pleasantness at faster velocities. Conversely, tactile intensity increased linearly with both increasing velocity and force. Recordings from seven C-LTMRs showed that their firing frequency changed with stroking velocity, but that they were strongly modulated by force. Overall, we demonstrate the profound effect that force has on the perception of stroking touch, where tactile pleasantness and intensity appear to be a multi-faceted construct that are related to C-LTMR and A{beta}-LTMR activity, respectively, but that the firing in individual mechanoreceptor populations cannot fully account for percepts.

neuroscience↗

bioRxiv2024

Molecular Identification and Antimicrobial Potential of Endophytic Fungi Against Some Grapevine Pathogens

In the Duhok province of the Kurdistan region, the plant species Vitis vinifera has been explored as a habitat for diverse endophytic microorganisms across various ecological environments. During the period from 2021 to 2022, a total of 600 samples were collected from four distinct locations: Bagera, Besfke, Barebhar, and Atrush. From these samples, twelve endophytic fungal species were isolated, including Aspergillus flavus, Botryosphaeria dothidea, Fusarium oxysporum, Fusarium ruscicol, Fusarium venenatum, Chaetomium globosum, Clonostachys rosea, Mucor racemosus, Penicillium glabrum, Aspergillus terreus, Aspergillus ustus, and Aspergillus niger. These fungi are recognized for their potential as biocontrol agents against grapevine trunk diseases and grape rotting fungi, which pose significant risks to grapevine health and productivity. Based on ITS and SSU sequencing, molecular identification confirmed these fungis presence with sequence identities ranging from 99% to 100%. Phylogenetic analysis revealed that these endophytes could be categorized into five main clusters (A, B, C, D, and E), showing high intra-group similarity. Utilizing the Dual Culture method, the endophyte Paecilomyces maximus demonstrated a 70.83% inhibition rate against Ilyonectria destructans. In the Food Poisoning method, A. flavus and P. maximus emerged as the most effective inhibitors of Ilyonectria destructans, whereas A. terreus, M. racemosus, and P. maximus achieved complete inhibition (100%) of Botrytis cinerea. Additionally, M. racemosus was identified as the most effective biocontrol agent against Neoscytalidium dimidiatum.

microbiology↗

bioRxiv2023

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

neuroscience↗

bioRxiv2020

Elevated soluble Galectin-3 as a marker of chemotherapy efficacy in Breast cancer patients: a prospective study

PurposeGalectin-3 (Gal-3) is a glycan-binding lectin with a debated role in cancer progression due to its various functions and patterns of expression. The current study investigates the relationship between breast cancer prognosis and secreted Gal-3. MethodsBreast cancer patients with first time cancer diagnosis and no prior treatment (n=88) were placed in either adjuvant or neoadjuvant setting based on their treatment modality. Stromal and plasma Gal-3 levels were measured in each patient at the time of diagnosis and then throughout treatment using immunohistochemistry (IHC) and ELISA respectively. Healthy women (>18 years of age, n=63) were used to establish baseline levels of plasma Gal-3. Patients were followed for 84 months for disease free survival analysis. ResultsEnhanced levels of plasma (adjuvant) and stromal (neo-adjuvant) Gal-3 were found to be markers of chemotherapy efficacy. The patients with chemotherapy induced increase in extracellular Gal-3 had longer disease-free interval and significantly lower rate of recurrence during 84-month follow-up compared to patients with unchanged or decreased secretion. ConclusionThe findings support the use of plasma Gal-3 as a marker for chemotherapy efficacy when no residual tumor is visible through imaging. Furthermore, stromal levels in any remaining tumors post chemotherapy can also be used to predict long term prognosis in patients. Key pointsIncreased Gal-3 secretion due to chemotherapy leads to better prognosis and longer disease-free survival. The analysis of soluble Gal-3 expression could be useful as a support tool in predicting treatment efficacy in patients with no visible tumor remaining for follow up through imaging.

cancer biology↗