The effect of methanolic extract of cashewnut (Anacardium occidentale) shell extract was seen on conidial germination of Erysiphe pisi and powdery mildew development in pea (Pisum sativum). Maximum conidial germination inhibition of E. pisi on glass slides was observed at 300 ppm. Similar effect on floated pea leaves was observed after 48 h at the same concentration. Conidial germination on intact untreated pea leaves was also assessed on II and IV nodal leaves while IV and II nodal leaves were treated with the extract and vice versa. There was tremendous reduction in conidial germination on all the nodal leaves. The disease intensity of pea powdery mildew was significantly reduced by methanolic extract of cashewnut shells. Maximum reduction was observed with 200 ppm where 39% disease intensity was recorded in comparison to 96.53% in the control. The phenolic acid content of pea leaves following treatments with this extract varied and no definite pattern was observed. Out of several phenolic compounds, namely, gallic, ferulic, chlorogenic, and cinnamic acids, only gallic acid was found to be present consistently in all the treatments with varied amounts.
Anacardium ; Gallic Acid ; Germination ; Glass ; Hydroxybenzoates ; Methanol ; Peas ; Phenol

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Bladder cancer remains a significant health challenge. Early and accurate diagnoses are crucial for effective treatment and improved patient outcomes. In recent years, artificial intelligence (AI) has emerged as a powerful tool in the medical field, showing great promise in advancing the bladder cancer diagnosis. This review explores the current state and potential of AI technologies, including machine learning algorithms, deep learning networks, and computer vision, in enhancing the diagnostic process for bladder cancer. AI systems can analyze vast amounts of data from various sources, such as medical imaging, genomic data, and electronic health records, enabling the identification of subtle patterns and biomarkers that may indicate the presence of bladder cancer. These systems have demonstrated high accuracy in detecting cancerous lesions in imaging modalities such as cystoscopy, ultrasonography, and computed tomography scans, often surpassing human performance. Moreover, AI-driven diagnostic tools can assist in risk stratification, predicting disease progression, and personalizing treatment plans, thereby contributing to more targeted and effective therapies.

Methods: A surgical database was retrospectively reviewed for patients undergoing primary, single-level MIS LD from 2013 to 2020. Patients lacking preoperative narcotic consumption data were excluded. Demographics, spinal pathologies, and operative characteristics were collected. Patients were grouped based on preoperative narcotic consumption. Patient Health Questionnaire-9 (PHQ-9), Visual Analog Scale (VAS) for back and leg, Oswestry Disability Index (ODI), 12-item Short Form Physical Component Summary, and Patient-Reported Outcomes Measurement Information System physical function (PROMIS-PF) were collected preoperatively and postoperatively. Preestablished values were used to calculate achievement of minimum clinically important difference (MCID). Differences in mean PROs and MCID achievement between groups were evaluated. Results: The cohort was 453 patients; 184 used preoperative narcotics and 269 did not. Significant differences were found in American Society of Anesthesiologists classification, ethnicity, insurance type, and estimated blood loss between groups. Significant differences were also found in preoperative PHQ-9, VAS leg, ODI, and PROMIS-PF between groups (all p<0.05). Mean postoperative PROs did not differ by group (p>0.05). A higher rate of MCID achievement was associated with the narcotic group for PHQ-9 and PROMIS-PF at 6 weeks (both p≤0.050), VAS leg at 1 year (p=0.009), and overall for ODI and PHQ-9 (both p≤0.050). Conclusions Preoperative narcotic consumption was associated with worse preoperative depression, leg pain, disability, and physical function. In patients consuming preoperative narcotics, a higher proportion achieved an overall MCID for disability and depressive symptoms. Patients taking preoperative narcotic medications may report significantly worse preoperative PROs but demonstrate greater improvements in postoperative disability and mental health.

Methods: A surgical database was reviewed for eligible patients who underwent lumbar fusion. Patient satisfaction and Visual Analog Scale (VAS) for back and leg pain were the outcomes of interest. Meeting expectations was calculated as a difference of ≤0 between preoperative expectations and postoperative VAS scores. MCID achievement was calculated by comparing changes in VAS scores with established values. Meeting preoperative expectations or MCID achievement as predictors of patient satisfaction was evaluated using regression analysis. Results: A total of 134 patients were included in this study. Patients demonstrated significant improvements in VAS back and VAS leg (p<0.001). At 1 year, 56.4% of patients had their VAS back expectations met compared with 59.5% for VAS leg. Similarly, at 1 year, 77.3% and 71.3% of patients achieved MCID for VAS back and leg, respectively. Meeting expectations for VAS back was significantly associated with patient satisfaction at all postoperative timepoints; however, MCID achievement only demonstrated a significant association with patient satisfaction at 6 and 12 weeks (all, p≤0.024). Meeting VAS leg expectations and MCID achievement both demonstrated a significant association with patient satisfaction at all postoperative timepoints (all, p≤0.02). No differences between MCID achievement and meeting expectations as predictors of satisfaction were noted. Conclusions The majority of patients achieved MCID and had their back and leg pain expectations met by 1 year. Both measures were significant predictors of patient satisfaction and suggest that MCID achievement may act as a suitable substitute for patient satisfaction.

Methods: A surgical database was reviewed to identify patients undergoing cervical spine procedures. Demographics, operative characteristics, comorbidities, NDI, Visual Analog Scale (VAS), and 12-item Short Form (SF-12) physical and mental composite scores (PCS and MCS) were recorded. NDI severity was categorized using previously established threshold values. Improvement from preoperative scores at each postoperative timepoint and convergent validity of NDI was evaluated. Discriminant validity of NDI was evaluated against VAS neck and arm and SF-12 PCS and MCS. Results: All 290 patients included in the study demonstrated significant improvements from baseline values for all patient-reported outcome measures (PROMs) at all postoperative timepoints (p<0.001) except SF-12 MCS at 2 years (p =0.393). NDI showed a moderate- to-strong correlation (r≥0.419) at most timepoints for VAS neck, VAS arm, SF-12 PCS, and SF-12 MCS (p<0.001, all). NDI severity categories demonstrated significant differences in mean VAS neck, VAS arm, SF-12 PCS, and SF-12 MCS at all timepoints (p<0.001, all). Differences between NDI severity groups were not uniform for all PROMs. VAS neck values demonstrated significant intergroup differences at most timepoints, whereas SF-12 MCS showed significantly different values between most severity groups. Conclusions Neck disability is strongly correlated with neck and arm pain, physical function, and mental health and demonstrates worse outcomes with increasing severity. Previously established severity categories may be more applicable to pain than physical function or mental health and may be more uniformly applied preoperatively for cervical spine patients.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.

Objective: To evaluate preoperative disability’s influence on patient-reported outcomes (PROs) following surgery for degenerative spondylolisthesis (DS). Methods: DS patients who underwent surgical intervention were retrospectively identified from a single-surgeon spine registry. Cohorts based on Oswestry Disability Index (ODI) < 41 (milder disability) and ≥ 41 (severe disability) were created. Demographic differences were accounted for with 1:1 propensity score matching. For the matched sample, perioperative and PRO data were additionally collected. PROs assessed included mental health, physical function, pain, and disability. Pre- and up to 2-year postoperative PROs were utilized. Average time to final follow-up was 15.7 ± 8.8 months. Improvements in PROs and minimal clinically important difference (MCID) rates were calculated. Continuous variables were compared through Student t-test and categorical variables were compared through chi-square tests. Results: Altogether, 214 patients were included with 77 in the milder disability group. The severe disability group had worse postoperative day (POD) 1 pain scores and longer hospital stays (p ≤ 0.038, both). The severe disability group reported worse outcomes pre- and postoperatively (p < 0.011, all), but had greater average improvement in 12-item Short Form health survey mental composite score (SF-12 MCS), 9-Item Patient Health Questionnaire (PHQ-9), visual analogue scale (VAS)-back, and ODI by 6 weeks (p ≤ 0.037, all) and PHQ-9, VAS-back and ODI by final follow-up (p ≤ 0.015, all). The severe disability cohort was more likely to achieve MCID for SF-12 MCS, PHQ-9, and ODI (p ≤ 0.003, all). Conclusion Patients with greater baseline disability report higher POD 1 pain and discharge later than patients with milder disability. While these patients report inferior physical/mental health before and after surgery, they report greater improvements in mental health and disability postoperatively.