'To live and die [for] Dixie': Irish civilians and the Confederate States of America

Around 20,000 Irishmen served in the Confederate army in the Civil War. As a result, they left behind, in various Southern towns and cities, large numbers of friends, family, and community leaders. As with native-born Confederates, Irish civilian support was crucial to Irish participation in the Confederate military effort. Also, Irish civilians served in various supporting roles: in factories and hospitals, on railroads and diplomatic missions, and as boosters for the cause. They also, however, suffered in bombardments, sieges, and the blockade. Usually poorer than their native neighbours, they could not afford to become 'refugees' and move away from the centres of conflict. This essay, based on research from manuscript collections, contemporary newspapers, British Consular records, and Federal military records, will examine the role of Irish civilians in the Confederacy, and assess the role this activity had on their integration into Southern communities. It will also look at Irish civilians in the defeat of the Confederacy, particularly when they came under Union occupation. Initial research shows that Irish civilians were not as upset as other whites in the South about Union victory. They welcomed a return to normalcy, and often 'collaborated' with Union authorities. Also, Irish desertion rates in the Confederate army were particularly high, and I will attempt to gauge whether Irish civilians played a role in this. All of the research in this paper will thus be put in the context of the Drew Gilpin Faust/Gary Gallagher debate on the influence of the Confederate homefront on military performance. By studying the Irish civilian experience one can assess how strong the Confederate national experiment was. Was it a nation without a nationalism

Genomic analyses in Cornelia de Lange Syndrome and related diagnoses: Novel candidate genes, <scp>genotype–phenotype</scp> correlations and common mechanisms

Cornelia de Lange Syndrome (CdLS) is a rare, dominantly inherited multisystem developmental disorder characterized by highly variable manifestations of growth and developmental delays, upper limb involvement, hypertrichosis, cardiac, gastrointestinal, craniofacial, and other systemic features. Pathogenic variants in genes encoding cohesin complex structural subunits and regulatory proteins (NIPBL, SMC1A, SMC3, HDAC8, and RAD21) are the major pathogenic contributors to CdLS. Heterozygous or hemizygous variants in the genes encoding these five proteins have been found to be contributory to CdLS, with variants in NIPBL accounting for the majority (&gt;60%) of cases, and the only gene identified to date that results in the severe or classic form of CdLS when mutated. Pathogenic variants in cohesin genes other than NIPBL tend to result in a less severe phenotype. Causative variants in additional genes, such as ANKRD11, EP300, AFF4, TAF1, and BRD4, can cause a CdLS‐like phenotype. The common role that these genes, and others, play as critical regulators of developmental transcriptional control has led to the conditions they cause being referred to as disorders of transcriptional regulation (or “DTRs”). Here, we report the results of a comprehensive molecular analysis in a cohort of 716 probands with typical and atypical CdLS in order to delineate the genetic contribution of causative variants in cohesin complex genes as well as novel candidate genes, genotype–phenotype correlations, and the utility of genome sequencing in understanding the mutational landscape in this population

Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74·0%) had emergency surgery and 280 (24·8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26·1%) patients. 30-day mortality was 23·8% (268 of 1128). Pulmonary complications occurred in 577 (51·2%) of 1128 patients; 30-day mortality in these patients was 38·0% (219 of 577), accounting for 81·7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1·75 [95% CI 1·28–2·40], p\textless0·0001), age 70 years or older versus younger than 70 years (2·30 [1·65–3·22], p\textless0·0001), American Society of Anesthesiologists grades 3–5 versus grades 1–2 (2·35 [1·57–3·53], p\textless0·0001), malignant versus benign or obstetric diagnosis (1·55 [1·01–2·39], p=0·046), emergency versus elective surgery (1·67 [1·06–2·63], p=0·026), and major versus minor surgery (1·52 [1·01–2·31], p=0·047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

Negative Affect Heightens Opiate Withdrawal-Induced Hyperalgesia in Heroin Dependent Individuals

This study examined the effect of emotion on opiate withdrawal induced hyperalgesia to determine whether emotional states modulate the magnitude of hyperalgesia. One hundred Hispanic men were recruited into one of three groups: heroin withdrawal, long-term heroin abstinence, and control. Participants were presented with pictures to induce neutral, positive, and negative emotional states. Affective valence, arousal, pain threshold, and tolerance to ischemic pain were measured. When pain threshold and tolerance were compared, the withdrawal group displayed significant heightened pain sensitivity when negative affect was induced. The authors also found that former heroin addicts showed heightened pain sensitivity following months of abstinence

Optimal scoring of the Multidimensional Pain Inventory in a chronic pain sample

The Multidimensional Pain Inventory (MPI) is one of the most commonly used self-report instruments in pain settings. The MPI can be used to classify patients into three clusters or its nine scales can be treated as dimensions in efforts to understand patient heterogeneity. Previous research suggests the existence of a fourth cluster, whose members have been labeled 'repressors,' that emerges with the addition of a defensiveness scale to the MPI. The current paper compared the abilities of MPI cluster and dimensional models with and without a measure of defensiveness to capture variability in validating variables related to personality, psychopathology, physical functioning, and treatment outcome in a chronic pain sample. Results suggest that dimensional models consistently outperform cluster models in explaining variance in outcome variables, and that the addition of a measure of defensiveness increments the validity offered by the MPI scales. Implications for the assessment of pain patients are discussed

An Efficient Method of Identifying Major Depression and Panic Disorder In Primary Care

The research literature is replete with evidence of and concerns about the prevalence and undertreatment of mental disorders in primary care. Although screening, on its own, may not directly affect clinical outcomes, it is still the most efficient and effective way to identify psychologically distressed patients for either research purposes or to provide patients with or refer patients to appropriate care. The current study sought to establish the utility of the MHI-5 for the detection of patients suffering from major depression or panic disorder, two of the most common psychiatric conditions seen in primary care settings. This study was conducted in a family medicine clinic and 246 adult outpatients participated. Patients completed the Mental Health Index-5 (MHI-5) as the screening measure and the PRIME-MD Patient Health Questionnaire (PHQ) as the diagnostic instrument. ROC analyses indicated that a cut-off score of 23 on the MHI-5 yielded a sensitivity of 91% and a specificity of 58% for predicting provisional diagnoses of major depression or panic disorder from the PHQ. Using a single item to screen for a PHQ diagnosis of major depression yielded a sensitivity of 88% and a specificity of 62% and a second question had a sensitivity of 100% and specificity of 63% for PHQ diagnosis of panic disorder. These results indicate that it is possible to use a small number of items to efficiently and effectively screen for mental disorders affecting a significant portion of primary care patients

Predicting the completion of an integrative and intensive outpatient chronic pain treatment with the personality assessment inventory

The effects of intensive, integrative treatments for chronic pain are affected by patient compliance, and in many cases, selecting noncompliant individuals adversely impacts the cost-effectiveness of such programs. The pretreatment identification of individuals who are at risk for dropout could assist clinicians in augmenting treatments with motivational enhancement strategies for high-risk patients or using such information to select individuals who are most likely to complete a given intervention program. In this study, we tested the ability of indicators from the Personality Assessment Inventory (PAI; Morey, 1991), administered prior to treatment, to identify individuals who dropped out of a 20-day chronic pain program. Results replicate findings from outpatient psychotherapy research in finding that PAI Mean Clinical Elevation and Treatment Process Index significantly differentiated dropouts from graduates, particularly when the Treatment Rejection scale suggested patients were motivated for treatment. We discuss these results and offer recommendations for the prediction of treatment dropout in pain settings

The convergence and predictive validity of the Multidimensional Pain Inventory and the Personality Assessment Inventory among individuals with chronic pain

Objective: To explore the convergence, redundancy, and validity of the Multidimensional Pain Inventory (MPI) and the Personality Assessment Inventory (PAI) in a chronic pain treatment setting. Participants: Data from intake (N=235) and follow-up (N=187) for individuals with an average of 9 years of chronic pain who participated in a 20-day integrative treatment program were analyzed. Outcome Measures: Oswestry Disability Index, Beck Depression and Anxiety inventories, Rand Short-Form Health Survey, and clinician-rated ability to stand and carry. Results: Conjoint factor analyses suggested that the MPI and PAI combine to tap five orthogonal factors: Negative Affect, Support, Externalizing, Physical Dysfunction, and Impulsivity. MPI and PAI scales significantly related to various aspects of client functioning, although these scales were more limited in predicting clinician-rated markers and change during treatment. Conclusion: Results support the combined use of the MPI and PAI to understand patient heterogeneity and predict treatment outcome in chronic pain samples