"If it has happened once, it can happen again". The impact of previous pregnancy loss on anxious women's ongoing pregnancies: A qualitative study from Pakistan.

BackgroundPregnancy loss that includes both miscarriage and stillbirth cause significant psychological distress for women including anxiety, depression, and grief that persist long after physical recovery. This study focuses on the experiences of women in Pakistan, where pregnancy loss rates are high.ObjectiveTo explore how pregnant women with anxiety symptoms and a history of pregnancy loss perceive their past experiences with the loss and how it affects overall well-being in their current pregnancy.DesignQualitative methods were used to explore the impact of previous pregnancy loss on the well-being of pregnant women.SettingThis qualitative research was embedded within a randomized control trial conducted in a tertiary care facility in Rawalpindi, Pakistan.ParticipantsData were collected through in-depth interviews with 18 pregnant women who had experienced pregnancy loss. Data was analyzed using Framework Analysis.FindingsThe findings revealed several factors influencing participants' well-being during pregnancies that resulted in a loss, such as unsupportive and abusive environments, unintended pregnancies, certain superstitious beliefs, poor health, and lack of access to quality healthcare. The study also highlighted the adverse impact of previous pregnancy loss on the ongoing pregnancy, including deterioration of physical and mental health and aversion of healthcare services. However, some participants reported positive changes in medical and self-care practices and an enhanced faith and reliance on destiny in their subsequent pregnancies.ConclusionOur study highlights the lasting impact of past pregnancy loss on subsequent pregnancies, affecting overall wellbeing and leading to healthcare avoidance. We identified persistent anxiety along with positive outcomes like enhanced medical practices and strengthened faith. Results suggest the need for culturally responsive interventions to support the overall well-being of anxious pregnant women with a history of pregnancy loss in resource-constrained settings

The Impact of the COVID-19 Pandemic on Pregnant Women with Perinatal Anxiety Symptoms in Pakistan: A Qualitative Study

The impact of coronavirus disease 2019 (COVID-19) on people with existing mental health conditions is likely to be high. We explored the consequences of the pandemic on women of lower socioeconomic status with prenatal anxiety symptoms living in urban Rawalpindi, Pakistan. This qualitative study was embedded within an ongoing randomized controlled trial of psychosocial intervention for prenatal anxiety at a public hospital in Rawalpindi. The participants were women with symptoms of anxiety who had received or were receiving the intervention. In total, 27 interviews were conducted; 13 women were in their third trimester of pregnancy, and 14 were in their postnatal period. The data were collected through in-depth interviews and analyzed using framework analysis. Key findings were that during the pandemic, women experienced increased perinatal anxiety that was linked to greater financial problems, uncertainties over availability of appropriate obstetric healthcare, and a lack of trust in health professionals. Women experienced increased levels of fear for their own and their baby’s health and safety, especially due to fear of infection. COVID-19 appears to have contributed to symptoms of anxiety in women already predisposed to anxiety in the prenatal period. Efforts to address women’s heightened anxiety due to the pandemic are likely to have public health benefits.</jats:p

Tomato Disease Classification using Fine-Tuned Convolutional Neural Network

Tomatoes have enhanced vitamins that are necessary for mental and physical health. We use tomatoes in our daily life. The global agricultural industry is dominated by vegetables. Farmers typically suffer a significant loss when tomato plants are affected by multiple diseases. Diagnosis of tomato diseases at an early stage can help address this deficit. It is difficult to classify the attacking disease due to its range of manifestations. We can use deep learning models to identify diseased plants at an initial stage and take appropriate measures to minimize loss through early detection. For the initial diagnosis and classification of diseased plants, an effective deep learning model has been proposed in this paper. Our deep learning-based pre-trained model has been tuned twofold using a specific dataset. The dataset includes tomato plant images that show diseased and healthy tomato plants. In our classification, we intend to label each plant with the name of the disease or healthy that is afflicting it. With 98.93% accuracy, we were able to achieve astounding results using the transfer learning method on this dataset of tomato plants. Based on our understanding, this model appears to be lighter than other advanced models with such considerable results and which employ ten classes of tomatoes. This deep learning application is usable in reality to detect plant diseases. Full Tex

Tomato Disease Classification using Fine-Tuned Convolutional Neural Network

Tomatoes have enhanced vitamins that are necessary for mental and physical health. We use tomatoes in our daily life. The global agricultural industry is dominated by vegetables. Farmers typically suffer a significant loss when tomato plants are affected by multiple diseases. Diagnosis of tomato diseases at an early stage can help address this deficit. It is difficult to classify the attacking disease due to its range of manifestations. We can use deep learning models to identify diseased plants at an initial stage and take appropriate measures to minimize loss through early detection. For the initial diagnosis and classification of diseased plants, an effective deep learning model has been proposed in this paper. Our deep learning-based pre-trained model has been tuned twofold using a specific dataset. The dataset includes tomato plant images that show diseased and healthy tomato plants. In our classification, we intend to label each plant with the name of the disease or healthy that is afflicting it. With 98.93% accuracy, we were able to achieve astounding results using the transfer learning method on this dataset of tomato plants. Based on our understanding, this model appears to be lighter than other advanced models with such considerable results and which employ ten classes of tomatoes. This deep learning application is usable in reality to detect plant diseases. Full Tex

Non-specialist-delivered psychosocial intervention for prenatal anxiety in a tertiary care setting in Pakistan: a qualitative process evaluation.

ObjectivesA manualised cognitive-behavioural therapy-based psychosocial intervention for prenatal anxiety called Happy Mother Healthy Baby is being tested for its effectiveness through a randomised control trial in Pakistan. The aim of this study was to evaluate the intervention delivery process and the research process.DesignQualitative methods were used to explore in depth the intervention delivery and research process.SettingThis process evaluation was embedded within a randomised control trial conducted in a tertiary care facility in Rawalpindi, Pakistan.ParticipantsData were collected through in-depth interviews (n=35) with the trial participants and focus group discussions (n=3) with the research staff. Transcripts were analysed using a Framework Analysis.ResultsThe evaluation of the intervention delivery process indicated that it can be effectively delivered by non-specialist providers trained and supervised by a specialist. The intervention was perceived to be culturally acceptable and appropriately addressing problems related to prenatal anxiety. Lack of awareness of 'talking' therapies and poor family support were potential barriers to participant engagement. The evaluation of the research process highlighted that culturally appropriate consent procedures facilitated recruitment of participants, while incentivisation and family involvement facilitated sustained engagement and retention. Lack of women's empowerment and mental health stigma were potential barriers to implementation of the programme.ConclusionWe conclude that non-specialists can feasibly deliver an evidence-based intervention integrated into routine antenatal care in a tertiary hospital. Non-specialist providers are likely to be more cost effective and less stigmatising. Inclusion of family is key for participant recruitment, retention and engagement with the intervention.Trial registration numberNCT03880032