Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: A cross-sectional regional study

Background: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew\u27s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features\u27 importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Hesitancy towards COVID-19 Vaccines: An Analytical Cross–Sectional Study

Vaccination is the most promising strategy to counter the spread of Coronavirus Disease 2019 (COVID-19). Vaccine hesitancy is a serious global phenomenon, and therefore the aim of this cross-sectional study was to explore the effect of educational background, work field, and social media on attitudes towards vaccination in Jordan. We compared between medical personnel who were in direct contact with patients and non-medical individuals at Jordan University Hospital in terms of demographics, knowledge about COVID-19 vaccines, rumors received via social media, their trust in these vaccines, and the encouraging factors for vaccination. 646 individuals were enrolled in this study, of which 287 (44.4%) were from medical field, and 359 (55.6%) from non-medical field. 226 (35%) were planning to take the vaccine once available, with a positive response from 131 (45.6%) medical field workers, compared to 94 (26.2%) non-medical individuals (p < 0.001). The social media rumor that was believed the most was the unsafety of these vaccines (n = 283; 43.8%). Only 163 (56.8%) of medical persons did not believe any of the circulated rumors, compared to 126 (35.1%) of non-medical persons (p < 0.001). The effect of medical personnel advice (OR = 0.83; 95% CI = 0.70 to 0.98; p = 0.026) and social media (OR = 1.21; 95% CI = 1.04 to 1.41; p = 0.012) were significantly associated with the willingness to take COVID-19 vaccine once available. In conclusion, medical personnel and social media play a crucial role in increasing the society’s inclination towards vaccination by providing the community with updated evidence-based information about COVID-19 vaccines as an efficient medical countermeasure and by correcting the previously spread misinformation

PACE4 cleaves PRR intracellularly in prostate cancer cells.

(A) Western blot analysis showing Prorenin Receptor (PRR), furin, and PACE4 expression, sPRR secretion, and cell lysate and conditioned media total lane protein (CLTLP, CMTLP) in LNCaP cells infected with a control non-target shRNA (NT), Furin shRNA (shfurin), or PACE4 shRNA (shPACE4). (B) Western Blot analysis demonstrating the expression of PRR and secretion of sPRR in LNCaP cells infected with an empty pLenti6 vector or with pLenti6-PACE4 to overexpress PACE4. (C) Quantification of sPRR levels in pLenti6 and pLenti-PACE4-infected LNCaP cells (*PD) Western blot showing the reduction in PRR processing resembled as a ratio of HA-tagged M8.9 (M8.9-HA) to HA-tagged full-length PRR (PRR-HA) in cellular extract of LNCaP cells after a 50 μM PACE4 inhibitor [33] LLLRVK-amidinobenzylamide (Amba) (C23) treatment. (E) Corresponding quantification of the ratio of M8.9-HA to PRR-HA standardized over total lane protein (TLP) (*PF) Analysis of PRR peptide cleavage by recombinant PACE4 (rPACE4) or recombinant furin (rfurin) monitored after a 2-hour incubation by high pressure liquid chromatography (HPLC). Mass spectrometry was done to confirm identity of peptide after cleavage. Cleavage site is underlined on the peptide sequence. Western blot analysis of PRR expression and sPRR secretion and quantification of sPRR secretion after DMSO (Vehicle), 50 μM multi-Leucine peptide (ML) PACE4 inhibitor, or 50 μM PEGylated cell-impermeable ML (PEG8-ML) treatment of DU145 (***PG, H) or LNCaP (I, J) (**P<0.01, n = 3) cells, respectively. Beta-Actin (β-actin) and TLP were used as loading controls. Data are presented as the mean ± SEM. Statistical tests were conducted using Student’s t test.</p

Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study

Background : Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms. Results : The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew’s Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features’ importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression. Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries

PTEN controls PRR processing and PACE4 protein expression in mouse.

(A) Western blot analysis of PTEN, PRR, and sPRR in homogenized WT, Pten-/-, Pten-/-/14-3-3σ-/-, and 14-3-3σ-/- mouse prostate tissue. Total lane protein (TLP) was used as a loading control. Dot plots of (B) Prorenin Receptor (PRR) and (C) PACE4 mRNA expression levels in wild type (WT), Pten-/-, Pten-/-/14-3-3σ -/-, and 14-3-3σ-/- prostate tissue (*P D) Corresponding representative IHC images of WT, Pten-/-, Pten-/-/14-3-3σ-/-, and 14-3-3σ-/- mouse prostate sections after H&E, PRR, and PACE4 staining. Scale bar measures 70 μm.</p

S1 Raw images -

Phosphatase and tensin homolog (PTEN) mutation is common in prostate cancer during progression to metastatic and castration resistant forms. We previously reported that loss of PTEN function in prostate cancer leads to increased expression and secretion of the Prorenin Receptor (PRR) and its soluble processed form, the soluble Prorenin Receptor (sPRR). PRR is an essential factor required for proper assembly and activity of the vacuolar-ATPase (V-ATPase). The V-ATPase is a rotary proton pump required for the acidification of intracellular vesicles including endosomes and lysosomes. Acidic vesicles are involved in a wide range of cancer related pathways such as receptor mediated endocytosis, autophagy, and cell signalling. Full-length PRR is cleaved at a conserved consensus motif (R-X-X-R↓) by a member of the proprotein convertase family to generate sPRR, and a smaller C-terminal fragment, designated M8.9. It is unclear which convertase processes PRR in prostate cancer cells and how processing affects V-ATPase activity. In the current study we show that PRR is predominantly cleaved by PACE4, a proprotein convertase that has been previously implicated in prostate cancer. We further demonstrate that PTEN controls PRR processing in mouse tissue and controls PACE4 expression in prostate cancer cells. Furthermore, we demonstrate that PACE4 cleavage of PRR is needed for efficient V-ATPase activity and prostate cancer cell growth. Overall, our data highlight the importance of PACE4-mediated PRR processing in normal physiology and prostate cancer tumorigenesis.</div

Full-length PRR expression increases and sPRR secretion decreases in the absence of PACE4 in mouse.

(A) Western blot analysis of prorenin receptor (PRR) and soluble prorenin receptor (sPRR) in prostate tissue extracted from wild type mice treated with saline, 2mg/Kg of PACE4 inhibitor [33] LLLRVK-amidinobenzylamide (Amba) (C23), and 4 mg/Kg C23. (B) Quantification of PRR processing in prostate tissue showing less PRR processing in prostate tissue extracted from mice treated with 2 mg/Kg and 4mg/Kg C23 (*PC) Quantitative PCR measurement of PACE4 expression in prostate, brain, and cerebellum tissue demonstrating the difference in PACE4 expression between wild type (WT) and Pace4-null (Pace4-/-) mice. (D) Western blot analysis of prostate, brain, and cerebellum tissue to analyze PRR expression in WT and Pace4-/- mice. (E) Corresponding quantification of PRR expression in the same tissues (*PF) ELISA quantification of plasma sPRR levels in WT (n = 4) and Pace4-/- (*P < 0.05, n = 8) mice. Beta-Actin (β-actin) and total lane protein were used as loading controls. Data are presented as the mean ±SEM. Statistical tests were conducted using Student’s t test.</p

PACE4 inhibition, like PRR knockdown, reduces V-ATPase activity.

(A) Representative proliferation images of LNCaP exposed to both DMSO or a 100 μM of PACE4 inhibitor [33] LLLRVK-amidinobenzylamide (Amba) (C23) over 0 and 64 hours. (B) Western blot analysis of soluble prorenin receptor (sPRR) secretion in conditioned media of LNCaP exposed to DMSO or a 100 μM C23. (C) Representative LysoTracker images of LNCaP cells transfected with non-silencing control siRNA (NSC), PRR siRNA 1 and 2, PACE siRNA, or treated with 100 nM Bafilomycin A1. (D) Quantification of LysoTracker signal intensity relative to number of cells in brightfield images treated with 1% DMSO (Vehicle), 50 μM C23, 100 nM Bafilomycin A1 (BafA1), or transfected with PRR siPRR 1 or siPRR 2. (E) Schematic showing the PRR-HA, sPRR-HA, and M8.9-HA vectors used in panels F, G and H. (F) Representative LysoTracker images of LNCaP cells transfected with empty vector (EV) and treated with DMSO, treated with 100 μM C23, transfected with PRR-HA and treated with 100 μM C23, transfected with M8.9-HA and treated with 100 μM C23, transfected with sPRR-HA and treated with 100 μM C23, or treated with 100 nM BafA1. (G) Corresponding quantification of LysoTracker intensity relative to cell number in the same treatments (**P ≤ 0.01, ***P ≤ 0.001, **** P H) Western blot analysis of PRR and HA expression in the same treatments. Total lane protein (TLP) was used as loading control. Data are presented as the mean ±SEM. Statistical tests were conducted using Student’s t test. Scale bar measures 100.</p

PTEN controls PACE4 expression and sPRR secretion in human prostate cancer cells, and both PACE4 and PRR expressions increase in human prostate tumor tissue.

(A) Western blot analysis showing less PACE4 endogenous protein expression after adenoviral Phosphatase and tensin homolog (PTEN) infection (PTEN-Ad) in PC3 and LNCaP cells. The same cells also exhibited less Prorenin Receptor (PRR) processing and less soluble Prorenin Receptor (sPRR) secretion in conditioned media (CM). Total lane protein (TLP) was used as a loading control. Quantification of (B) PACE4 expression and (C) sPRR secretion relative to the corresponding TLP in PC3 and LNCaP prostate cancer cells (*P D) Representative IHC tissue microarray images of PTEN, PACE4, and PRR staining on prostatic intraepithelial neoplasia (PIN) tissue and tumor tissue. Scale bar measures 100 μm. Dot plots representing the mean of the qualitative score assigned to (E) PTEN, (F) PACE4, and (G) PRR staining on PIN (n = 68) and tumor (n = 105) patient tissue microarrays (*PH) The Cancer Genome Atlas (TCGA) mRNA expression data in Log2 scale of PTEN, PCSK6 (PACE4), and ATP6AP2 (PRR) in tumor (T) and normal (N) prostate tissue.</p