Laser capture microdissection (LCM) and whole genome amplification (WGA) of DNA from normal breast tissue --- optimization for genome wide array analyses

<p>Abstract</p> <p>Background</p> <p>Laser capture microdissection (LCM) can be applied to tissues where cells of interest are distinguishable from surrounding cell populations. Here, we have optimized LCM for fresh frozen normal breast tissue where large amounts of fat can cause problems during microdissection. Since the amount of DNA needed for genome wide analyses, such as single nucleotide polymorphism (SNP) arrays, is often greater than what can be obtained from the dissected tissue, we have compared three different whole genome amplification (WGA) kits for amplification of DNA from LCM material. In addition, the genome wide profiling methods commonly used today require extremely high DNA quality compared to PCR based techniques and DNA quality is thus critical for successful downstream analyses.</p> <p>Findings</p> <p>We found that by using FrameSlides without glass backing for LCM and treating the slides with acetone after staining, the problems caused by excessive fat could be significantly decreased. The amount of DNA obtained after extraction from LCM tissue was not sufficient for direct SNP array analysis in our material. However, the two WGA kits based on Phi29 polymerase technology (Repli-g^®(Qiagen) and GenomiPhi (GE Healthcare)) gave relatively long amplification products, and amplified DNA from Repli-g^®gave call rates in the subsequent SNP analysis close to those from non-amplified DNA. Furthermore, the quality of the input DNA for WGA was found to be essential for successful SNP array results and initial DNA fragmentation problems could be reduced by switching from a regular halogen lamp to a VIS-LED lamp during LCM.</p> <p>Conclusions</p> <p>LCM must be optimized to work satisfactorily in difficult tissues. We describe a work flow for fresh frozen normal breast tissue where fat is inclined to cause problems if sample treatment is not adapted to this tissue. We also show that the Phi29-based Repli-g^®WGA kit (Qiagen) is a feasible approach to amplify DNA of high quality prior to genome wide analyses such as SNP profiling.</p

Prevalence and pattern of HIV-related malnutrition among women in sub-Saharan Africa: a meta-analysis of demographic health surveys

<p>Abstract</p> <p>Background</p> <p>The world's highest HIV infection rates are found in Sub-Saharan Africa (SSA), where adult prevalence in most countries exceeds 25%. Food shortages and malnutrition have combined with HIV/AIDS to bring some countries to the brink of crisis. The aim of this study was to describe prevalence of malnutrition among HIV-infected women and variations across socioeconomic status using data from 11 countries in SSA.</p> <p>Methods</p> <p>This study uses meta-analytic procedures to synthesize the results of most recent data sets available from Demographic and Health Surveys of 11 countries in SSA. Pooled prevalence estimates and 95% confidence intervals were calculated using random-and fixed-effects models. Subgroup and leave-one-country-out sensitivity analyses were also carried out.</p> <p>Results</p> <p>Pooling the prevalence estimates of HIV-related malnutrition yielded an overall prevalence of 10.3% (95% CI 7.4% to 14.1%) with no statistically significant heterogeneity (<it>I</it>²= 0.0%, p = .903). The prevalence estimates decreased with increasing wealth index and education attainment. The pooled prevalence of HIV-related malnutrition was higher among women residing in rural areas than among women residing in urban areas; and lower among women that were professionally employed than unemployed or women in agricultural or manual work.</p> <p>Conclusion</p> <p>Prevalence of HIV-related malnutrition among women varies by wealth status, education attainment, occupation, and type of residence (rural/urban). The observed socioeconomic disparities can help provide more information about population subgroups in particular need and high risk groups, which may in turn lead to the development and implementation of more effective intervention programs.</p

Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis

Data Availability Statement: The raw data supporting the conclusions of this article and code used for analysis will be made available by the authors on reasonable request pending appropriate study approvals and data transfer agreements between participating institutions.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/brainsci14070669/s1, Table S1: Inclusion/exclusion criteria for each data source; Table S2: Deficit in words recalled for each clinical condition relative to matched controls. Refs. [61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100] are cited in the Supplementary Materials.Deficits in memory performance have been linked to a wide range of neurological and neuropsychiatric conditions. While many studies have assessed the memory impacts of individual conditions, this study considers a broader perspective by evaluating how memory recall is differentially associated with nine common neuropsychiatric conditions using data drawn from 55 international studies, aggregating 15,883 unique participants aged 15–90. The effects of dementia, mild cognitive impairment, Parkinson’s disease, traumatic brain injury, stroke, depression, attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorder on immediate, short-, and long-delay verbal learning and memory (VLM) scores were estimated relative to matched healthy individuals. Random forest models identified age, years of education, and site as important VLM covariates. A Bayesian harmonization approach was used to isolate and remove site effects. Regression estimated the adjusted association of each clinical group with VLM scores. Memory deficits were strongly associated with dementia and schizophrenia (p 0.05). Differences associated with clinical conditions were larger for longer delayed recall duration items. By comparing VLM across clinical conditions, this study provides a foundation for enhanced diagnostic precision and offers new insights into disease management of comorbid disorders.This research was funded by the Psychological Health/Traumatic Brain Injury Research Program Long-Term Impact of Military Relevant Brain Injury Consortium (LIMBIC), Grant/Award Numbers: W81XWH18PH, TBIRPLIMBIC under Awards Numbers: W81XWH1920067 and W81XWH1320095; US Department of Defense, Grant/Award Number: AZ150145; US Department of Veterans Affairs, Grant/Award Numbers: I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX001135, I01 RX002076, I01 RX001880, I01 RX002172, I01 RX002173, I01 RX002171, I01 RX002174, I01 RX002170, 1I01 RX003444; National Institutes of Health (NIH), Grant/Award Number(s): RF1NS115268, RF1NS128961, U01NS086625, U01MH124639, P50MH115846, R01MH113827, R25MH080663, K08MH068540, R01NS100973, R01EB006841, P20GM103472, RO1MH083553, T32MH019535, R01 HD061504, RO1MH083553, R01AG050595, R01AG076838, R01AG060470, R01AG064955, P01AG055367, K23MH095661, R01MH094524, R01MH121246, T32MH019535, R01NS124585, R01NS122827, R61NS120249, R01NS122184, U54EB020403, R01MH116147, R56AG058854, P41EB015922, R01MH111671, P41RR14075, M01RR01066, R01EB006841, R01EB005846, R01 EB000840, RC1MH089257, U24 RR021992, and NCRR 5 month-RR001066 (MGH General Clinical Research Center); National Institute of Mental Health (NIMH), Grant/Award Number: 1P20RR021938; Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III, Grant/Award Numbers: PI15-00852, PI18-00945, JR19-00024, PI17-00481, PI20-00721; Sara Borrell contract, Grant/Award Number: CD19-00149; German Research Foundation DFG grant FOR2107, Grant/Award Numbers: JA 1890/7-1, JA 1890/7-2, NE2254/1-2, NE2254/2-1, NE2254/3-1, NE2254/4-1, KI588/14-1, KI588/14-2, DA1151/5-1, DA1151/5-2, SFB-TRR58, Projects C09 and Z02; European Union, NextGenerationEU, Grant/Award Numbers: PMP21/00051, PI19/01024; Structural Funds; Seventh Framework Program; H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking: Project PRISM-2, Grant/Award Number: 101034377; Project AIMS-2-TRIALS, Grant/Award Number: 777394; Horizon Europe; NSF, Grant/Award Number: 2112455; Madrid Regional Government, Grant/Award Number: B2017/BMD-3740 AGES-CM-2; Dalhousie Medical Research Foundation; Research Nova Scotia, Grant/Award Number: RNS-NHIG-2021-1931; NJ Commission on TBI Research Grants, Grant/Award Numbers: CBIR11PJT020, CBIR13IRG026; Department of Psychology, University of Oslo; Sunnaas Rehabilitation Hospital, Grant/Award Number: HF F32NS119285; Canadian Institutes of Health Research, Grant/Award Number: 166098; Neurological Foundation of New Zealand, Grant/Award Number: 2232 PRG; Canterbury Medical Research Foundation, University of Otago; Biogen US Investigator-initiated grant; Italian Ministry of Health, Grant/Award Number: RF-2019-12370182 and Ricerca Corrente RC 23; National Institute on Aging; National Health and Medical Research Council, Investigator Grant/Award Number: APP1176426; PA Health Research, Grant/Award Number: 4100077082; La Caixa Foundation, Grant/Award Number: 100010434, fellowship code: LCF/BQ/PR22/11920017; Research Council of Norway, Grant/Award Number: 248238; Health Research Council of New Zealand Sir Charles Hercus Early Career Development, Grant/Award Numbers: 17/039 and 14-440; Health Research Council of New Zealand, Grant/Award Numbers: 20/538 and 14/440; Research and Education Trust Pacific Radiology, Grant/Award Number: MRIJDA; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2018076; Norwegian ExtraFoundation for Health and Rehabilitation, Grant/Award Numbers: 2015/FO5146; South-Eastern Norway Regional Health Authority, Grant/Award Number: 2015044; Stiftelsen K.G. Jebsen, Grant/Award Number: SKGJ MED-02; The Liaison Committee between Central Norway Regional Health Authority (RHA) and the Norwegian University of Science and Technology (NTNU), Grant/Award Number: 2020/39645; National Health and Medical Research Council, Grant/Award Number: APP1020526; Brain Foundation; Wicking Trust; Collie Trust; Sidney and Fiona Myer Family Foundation; U.S. Army Medical Research and Materiel Command (USAMRMC), Grant/Award Number: 13129004; Department of Energy, Grant/Award Number: DE-FG02-99ER62764; Mind Research Network; National Association for Research in Schizophrenia and Affective Disorders, Young Investigator Award; Blowitz Ridgeway and Essel Foundations; Interdisciplinary Center for Clinical Research (IZKF) of the medical faculty of Münster; NOW ZonMw TOP, Grant/Award Number: 91211021; UCLA Easton Clinic for Brain Health; UCLA Brain Injury Research Center; Stan and Patty Silver; Clinical and Translational Research Center, Grant/Award Numbers: UL1RR033176, UL1TR000124; Mount Sinai Institute for NeuroAIDS Disparities; VA Rehab SPIRE; CDMRP PRAP; VA RR&D, Grant/Award Number: IK2RX002922; Veski Fellowship; Femino Foundation grant; Fundación Familia Alonso; Fundación Alicia Koplowitz; CIBERSAM, Madrid Regional Government, Grant/Award Numbers: B2017/BMD-3740 AGES-CM-2, 2019R1C1C1002457, 21-BR-03-01, 2020M3E5D9079910, 21-BR-03-01; Deutsche Forschungsgemeinschaft (DFG), Grant/Award Numbers: NE2254/1-2, NE2254/2-1, NE2254/3-1, NE2254/4-1

Effects of a Community Intervention on HIV Prevention Behaviors among Men Who Experienced Childhood Sexual or Physical Abuse in Four African Settings: Findings from NIMH Project Accept (HPTN 043)

There is increased focus on HIV prevention with African men who report experiencing childhood sexual (CSA) or physical abuse (CPA).To better understand the effects of a community-based intervention (Project Accept HPTN 043) on HIV prevention behaviors among men who report CSA or CPA experiences.Project Accept compared a community-based voluntary mobile counseling and testing (CBVCT) intervention with standard VCT. The intervention employed individual HIV risk reduction planning with motivational interviewing in 34 African communities (16 communities at 2 sites in South Africa, 10 in Tanzania, and 8 in Zimbabwe). Communities were randomized unblinded in matched pairs to CBVCT or SVCT, delivered over 36 months. The post-intervention assessment was conducted using a single, cross-sectional random survey of 18-32 year-old community members (total N = 43,292). We analyzed the effect of the intervention on men with reported CSA or CPA across the African sites. Men were identified with a survey question asking about having experienced CSA or CPA across the lifespan. The effect of intervention on considered outcomes of the preventive behavior was statistically evaluated using the logistic regression models.Across the sites, the rates of CSA or CPA among men indicated that African men reflected the global prevalence (20%) with a range of 13-24%. The statistically significant effect of the intervention among these men was seen in their increased effort to receive their HIV test results (OR 2.71; CI: (1.08, 6.82); P: 0.034). The intervention effect on the other designated HIV prevention behaviors was less pronounced.The effect of the intervention on these men showed increased motivation to receive their HIV test results. However, more research is needed to understand the effects of community-based interventions on this group, and such interventions need to integrate other keys predictors of HIV including trauma, coping strategies, and intimate partner violence

Recidivism in HIV-Infected Incarcerated Adults: Influence of the Lack of a High School Education

Recidivism is a pervasive problem facing the incarcerated. Incarcerated persons who are human immunodeficiency virus (HIV)-infected often have multiple risk factors associated with initial incarceration and recidivism, in particular, injection drug use. Yet, some jails provide case management for HIV-infected inmates to provide continuity of health care, which might have positive effects on reentry into the community. We sought to measure recidivism and factors related to recidivism in an HIV-infected cohort in an urban county jail with an active case management program. Fifty-two inmates surveyed in 1999 at the San Francisco County Jail were followed for rearrests through 2006. In follow-up, 73% were re-incarcerated on an average of 6.8 times for 552 days. Risk factors included nonwhite ethnicity, history of homelessness and crack use, common risk factors for incarceration. Less than high school education was associated with recidivism, shorter time to reincarceration, and more incarcerations. HIV-infected inmates spend a high proportion of time in multiple incarcerations, a reflection of the cyclical nature of incarceration despite comprehensive case management. Well-known risk factors for incarceration were associated with recidivism; in addition, lack of high school education played a prominent role. Education should be explored as a way to make further progress on breaking the cycle of incarceration

Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study.

Background: Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. Methods and Findings: The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction = 1.20x10(-4)). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction = 1.50x10(-3)) and waist circumference (p for interaction = 7.49x10(-9)). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. Conclusions: The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this subgroup is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention