Gene expression analysis of alkaline phosphatase in peri-implantitis tissue.

Peri-implantitis is a condition that causes inflammation and bone loss around dental implants. Alkaline phosphatase (ALP) is a gene involved in the inflammation of the tissue. Therefore, it is of interest to evaluate the expression of the ALP gene around the peri-implantitis site. 20 samples were collected and analyzed using qRT-PCR, and statistical analysis was performed. The results showed a significant decrease in ALP expression in infected peri-implantitis tissue compared to normal tissue, indicating that ALP gene is involved in the inflammation of peri-implantitis and correlates with clinical findings.

Evaluation of neo-implant abutment junction sealing gel among Indian patients.

With the advent of various implant abutment junctions, the ultimate aim is to develop a precise implant abutment junction with negligible micro-leakage. However precise the mechanical connection is, there seems to be a negligible amount of micro-leakage that is present that can be addressed with the help of sealing gel. This study aims to assess the micro-leakage between the neo-sealing gel and the commercially available implant sealing gel. The study was conducted on implants (n=15) with internal hex connections, group 1 (No gel, n=5), group 2 (Neo gel, n=5) and group 3 (commercial gel, n=5). Mean dE*ab values of 0.28± 0.02, 0.04± 0.01 and 0.17±0.01 were noted for the 3 groups, and there was statistically significant difference between the 3 groups (p≤0.05). The study suggests that the neo sealing gel may be a promising material to prevent bacterial ingress and micro-leakage at the implant abutment junction.

Correlation between CT - derived bone density and optimal bone densities acquired from CBCT scans.

It is of interest to explore the correlation between preoperative bone density, assessed via CBCTT, and primary stability of dental implants, assessed by torque ratchet. A total of 100 patients who had implant placed were taken a sample for this retrospective study. The Hounsfield units (HU) derived preoperative bone densities at implant sites that were acquired with the help of the CBCT and the primary stability was achieved during the day of surgery. Both were compared to optimum bone densities. Statistical correlation was done between the HU and Bone density. Data suggests that evaluating HU values, along with other parameters, before performing implant surgery could lead to better primary implant stability.

Investigating the impact age has on within-over and death bowling performances in international level 50-over cricket.

The aims of this investigation were to determine if ageing effects were present in elite international level cricket. Ball-by-ball data were analysed for 96 bowlers in the 50-Over World Cup 2019. Bowlers were categorized into 1 of 3 age groups GROUP 1 (18-24), GROUP 2 (25-31) and GROUP 3 (32+). Ordinal Logistic Regressions (OLR) and Multinomial Logistic Regressions (MLR) were conducted to estimate the relationship between age and bowling performances. OLR Results revealed that younger bowlers were significantly more likely to concede a greater number of runs on the last ball of their overs than their older counterparts (p = 0.001). A separate MLR analysis was conducted for those bowlers entrusted to bowl during the "death" phase of an innings, and results revealed that GROUP 2 bowlers were significantly more likely to take wickets than either GROUP 1 (p = 0.021) or GROUP 3 (p = 0.022) bowlers. The evidence indicates that wicket taking "death bowlers" will likely perform at their peak between the ages of 25-31, which could inform the bowling strategy of team captains. Furthermore, there is evidence to suggest that younger bowlers are not as skilled as their older counterparts at restricting runs conceded.

Seam or swing? Identifying the most effective type of bowling variation for fast bowlers in men's international 50-over cricket.

In this study, 13,176 balls bowled by international level fast bowlers were analysed in order to investigate the relationship between the types of delivery and their effectiveness. The results of Chi-Squared analyses revealed significant associations between the type of delivery and runs conceded (p < 0.001) as well as wickets taken (p < 0.001). Seam movement was revealed to be more effective than swing bowling at both producing dot balls and taking wickets. Specifically, balls that 'seam-away" were revealed to be the most effective for bowling dots and "seam-in" for taking wickets. The "away-swinger" resulted in significantly greater than expected dot balls as did the "in-swinger" but only the in-swinger resulted in significantly greater than expected wickets. Both the "off-cutter" and "slower-balls" were revealed to result in significantly fewer than expected dot balls but significantly greater than expected wickets, implying bowlers must assess for themselves the risk versus reward of these two types of variation. Balls with no-movement were revealed to have no significant relationship with runs conceded, but did result in significantly fewer than expected wickets. Evidence suggests that lateral movement is crucial to bowling success with seam movement revealed to be more effective than swing.

Context is key: normalization as a novel approach to sport specific preprocessing of KPI's for match analysis in soccer.

Key Performance Indicators (KPIs) have been investigated, validated and applied in multitude of sports for recruiting, coaching, opponent, self-analysis etc. Although a wide variety of in game performance indicators have been used as KPIs, they lack sports specific context. With the introduction of artificial intelligence and machine learning (AI/ML) in sports, the need for building intrinsic context into the independent variables is even greater as AI/ML models seem to perform better in terms of predictability but lack interpretability. The study proposes domain specific feature preprocessing method (normalization) that can be utilized across a wide range of sports and demonstrates its value through a specific data transformation by using team possession as a normalizing factor while analyzing defensive performance in soccer. The study performed two linear regressions and three gradient boosting machine models to demonstrate the value of normalization while predicting defensive performance. The results demonstrate that the direction of correlation of the relevant variables changes post normalization while predicting defensive performance of teams for the whole season. Both raw and normalized KPIs showing significant correlation with defensive performance (p < 0.001). The addition of the normalized variables contributes towards higher information gain, improved performance and increased interpretability of the models.

Using multiple machine learning algorithms to classify elite and sub-elite goalkeepers in professional men's football.

This study applied multiple machine learning algorithms to classify the performance levels of professional goalkeepers (GK). Technical performances of GK's competing in the elite divisions of England, Spain, Germany, and France were analysed in order to determine which factors distinguish elite GK's from sub-elite GK's. A total of (n = 14,671) player-match observations were analysed via multiple machine learning algorithms (MLA); Logistic Regressions (LR), Gradient Boosting Classifiers (GBC) and Random Forest Classifiers (RFC). The results revealed 15 common features across the three MLA's pertaining to the actions of passing and distribution, distinguished goalkeepers performing at the elite level from those that do not. Specifically, short distribution, passing the ball successfully, receiving passes successfully, and keeping clean sheets were all revealed to be common traits of GK's performing at the elite level. Moderate to high accuracy was reported across all the MLA's for the training data, LR (0.7), RFC (0.82) and GBC (0.71) and testing data, LR (0.67), RFC (0.66) and GBC (0.66). Ultimately, the results discovered in this study suggest that a GK's ability with their feet and not necessarily their hands are what distinguishes the elite GK's from the sub-elite.