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Complement-Targeted Therapies in Glioblastoma: A Systematic Review.

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The authors' code

Python · 513 lines · 17 KB · no license

  1. """
  2. Meta-analysis computations: pooled effect sizes, heterogeneity,
  3. forest plots, funnel plots, subgroup analysis, sensitivity analysis,
  4. Egger's test, SMD calculation, and HR pooling.
  5. """
  7. import logging
  8. from pathlib import Path
  9. from typing import Optional
  11. import numpy as np
  12. import pandas as pd
  13. import matplotlib.pyplot as plt
  14. from scipy import stats
  16. logger = logging.getLogger(__name__)
  19. # ---------------------------------------------------------------------------
  20. # Core pooling
  21. # ---------------------------------------------------------------------------
  23. def compute_pooled_effect(
  24. effects: np.ndarray,
  25. variances: np.ndarray,
  26. method: str = "random",
  27. ) -> dict:
  28. """
  29. Compute pooled effect size using inverse-variance weighting.
  30. DerSimonian-Laird for random effects.
  31. """
  32. effects = np.asarray(effects, dtype=float)
  33. variances = np.asarray(variances, dtype=float)
  35. mask = np.isfinite(effects) & np.isfinite(variances) & (variances > 0)
  36. effects = effects[mask]
  37. variances = variances[mask]
  39. if len(effects) < 2:
  40. return {"pooled_effect": float("nan"), "n_studies": len(effects),
  41. "error": "Need at least 2 studies"}
  43. weights_fixed = 1.0 / variances
  44. pooled_fixed = np.sum(weights_fixed * effects) / np.sum(weights_fixed)
  46. Q = np.sum(weights_fixed * (effects - pooled_fixed) ** 2)
  47. df = len(effects) - 1
  48. C = np.sum(weights_fixed) - np.sum(weights_fixed ** 2) / np.sum(weights_fixed)
  49. tau2 = max(0, (Q - df) / C) if C > 0 else 0
  51. I2 = max(0, (Q - df) / Q * 100) if Q > 0 else 0.0
  53. if method == "random":
  54. weights = 1.0 / (variances + tau2)
  55. else:
  56. weights = weights_fixed
  58. pooled = np.sum(weights * effects) / np.sum(weights)
  59. se_pooled = np.sqrt(1.0 / np.sum(weights))
  61. ci_lower = pooled - 1.96 * se_pooled
  62. ci_upper = pooled + 1.96 * se_pooled
  63. z = pooled / se_pooled if se_pooled > 0 else 0
  64. p_value = 2 * (1 - stats.norm.cdf(abs(z)))
  66. return {
  67. "pooled_effect": float(pooled),
  68. "se": float(se_pooled),
  69. "ci_lower": float(ci_lower),
  70. "ci_upper": float(ci_upper),
  71. "p_value": float(p_value),
  72. "z": float(z),
  73. "tau2": float(tau2),
  74. "I2": float(I2),
  75. "Q": float(Q),
  76. "Q_df": int(df),
  77. "Q_p": float(1 - stats.chi2.cdf(Q, df)) if df > 0 else 1.0,
  78. "method": method,
  79. "n_studies": int(len(effects)),
  80. }
  83. # ---------------------------------------------------------------------------
  84. # Hazard ratio pooling (log-HR scale)
  85. # ---------------------------------------------------------------------------
  87. def pool_hazard_ratios(
  88. hrs: np.ndarray,
  89. hr_ci_lowers: np.ndarray,
  90. hr_ci_uppers: np.ndarray,
  91. method: str = "random",
  92. ) -> dict:
  93. """
  94. Pool hazard ratios on the log scale.
  95. CIs are used to derive SE: SE(log HR) = (log(upper) - log(lower)) / (2 * 1.96)
  96. """
  97. hrs = np.asarray(hrs, dtype=float)
  98. lowers = np.asarray(hr_ci_lowers, dtype=float)
  99. uppers = np.asarray(hr_ci_uppers, dtype=float)
  101. mask = (hrs > 0) & (lowers > 0) & (uppers > 0) & np.isfinite(hrs)
  102. hrs = hrs[mask]
  103. lowers = lowers[mask]
  104. uppers = uppers[mask]
  106. if len(hrs) < 2:
  107. return {"pooled_hr": float("nan"), "n_studies": len(hrs),
  108. "error": "Need at least 2 studies with HR + CI"}
  110. log_hrs = np.log(hrs)
  111. se_log_hrs = (np.log(uppers) - np.log(lowers)) / (2 * 1.96)
  112. variances = se_log_hrs ** 2
  114. result = compute_pooled_effect(log_hrs, variances, method=method)
  116. result["pooled_hr"] = float(np.exp(result["pooled_effect"]))
  117. result["hr_ci_lower"] = float(np.exp(result["ci_lower"]))
  118. result["hr_ci_upper"] = float(np.exp(result["ci_upper"]))
  119. result["scale"] = "log-HR"
  121. return result
  124. def pool_hazard_ratios_with_se(
  125. hrs: np.ndarray,
  126. se_log_hrs: np.ndarray,
  127. method: str = "random",
  128. ) -> dict:
  129. """Pool HRs when SE of log(HR) is directly available."""
  130. hrs = np.asarray(hrs, dtype=float)
  131. se_log_hrs = np.asarray(se_log_hrs, dtype=float)
  133. mask = (hrs > 0) & (se_log_hrs > 0) & np.isfinite(hrs)
  134. hrs = hrs[mask]
  135. se_log_hrs = se_log_hrs[mask]
  137. if len(hrs) < 2:
  138. return {"pooled_hr": float("nan"), "n_studies": len(hrs),
  139. "error": "Need at least 2 studies"}
  141. log_hrs = np.log(hrs)
  142. variances = se_log_hrs ** 2
  144. result = compute_pooled_effect(log_hrs, variances, method=method)
  145. result["pooled_hr"] = float(np.exp(result["pooled_effect"]))
  146. result["hr_ci_lower"] = float(np.exp(result["ci_lower"]))
  147. result["hr_ci_upper"] = float(np.exp(result["ci_upper"]))
  148. result["scale"] = "log-HR"
  150. return result
  153. # ---------------------------------------------------------------------------
  154. # Prediction interval
  155. # ---------------------------------------------------------------------------
  157. def prediction_interval(
  158. pooled_effect: float,
  159. se_pooled: float,
  160. tau2: float,
  161. k: int,
  162. ) -> dict:
  163. """
  164. Compute the 95% prediction interval for a new study.
  165. PI = pooled +/- t(k-2, 0.975) * sqrt(se^2 + tau^2)
  166. Requires k >= 3.
  167. """
  168. if k < 3:
  169. return {
  170. "pi_lower": float("nan"),
  171. "pi_upper": float("nan"),
  172. "error": "Need at least 3 studies for prediction interval",
  173. }
  174. t_crit = stats.t.ppf(0.975, df=k - 2)
  175. pi_se = np.sqrt(se_pooled ** 2 + tau2)
  176. return {
  177. "pi_lower": float(pooled_effect - t_crit * pi_se),
  178. "pi_upper": float(pooled_effect + t_crit * pi_se),
  179. "t_critical": float(t_crit),
  180. "pi_se": float(pi_se),
  181. }
  184. # ---------------------------------------------------------------------------
  185. # Standardized mean difference (SMD / Hedges' g)
  186. # ---------------------------------------------------------------------------
  188. def compute_smd(
  189. mean_t: float, sd_t: float, n_t: int,
  190. mean_c: float, sd_c: float, n_c: int,
  191. ) -> dict:
  192. """Compute Hedges' g (bias-corrected SMD) for a single study."""
  193. sp = np.sqrt(((n_t - 1) * sd_t ** 2 + (n_c - 1) * sd_c ** 2) / (n_t + n_c - 2))
  195. if sp == 0:
  196. return {"smd": 0, "se_smd": float("nan"), "variance": float("nan")}
  198. d = (mean_t - mean_c) / sp
  200. # Hedges' correction
  201. df = n_t + n_c - 2
  202. j = 1 - 3 / (4 * df - 1) if df > 1 else 1
  203. g = d * j
  205. var_g = (n_t + n_c) / (n_t * n_c) + g ** 2 / (2 * (n_t + n_c))
  206. se_g = np.sqrt(var_g)
  208. return {"smd": float(g), "se_smd": float(se_g), "variance": float(var_g)}
  211. def pool_smds(
  212. smds: np.ndarray,
  213. variances: np.ndarray,
  214. method: str = "random",
  215. ) -> dict:
  216. """Pool standardized mean differences."""
  217. result = compute_pooled_effect(smds, variances, method=method)
  218. result["metric"] = "SMD (Hedges' g)"
  219. return result
  222. # ---------------------------------------------------------------------------
  223. # Subgroup analysis
  224. # ---------------------------------------------------------------------------
  226. def subgroup_analysis(
  227. df: pd.DataFrame,
  228. effect_col: str,
  229. variance_col: str,
  230. group_col: str,
  231. method: str = "random",
  232. ) -> dict:
  233. """Run pooled analysis within each subgroup and test for differences."""
  234. results = {}
  235. for group, group_df in df.groupby(group_col):
  236. effects = group_df[effect_col].values
  237. variances = group_df[variance_col].values
  238. if len(effects) >= 2:
  239. results[str(group)] = compute_pooled_effect(effects, variances, method)
  240. results[str(group)]["n_studies"] = len(effects)
  242. # Test for subgroup differences (Q_between)
  243. if len(results) >= 2:
  244. pooled_effects = np.array([r["pooled_effect"] for r in results.values()])
  245. pooled_weights = np.array([1 / r["se"] ** 2 for r in results.values() if r["se"] > 0])
  246. if len(pooled_weights) >= 2:
  247. overall = np.sum(pooled_weights * pooled_effects[:len(pooled_weights)]) / np.sum(pooled_weights)
  248. Q_between = np.sum(pooled_weights * (pooled_effects[:len(pooled_weights)] - overall) ** 2)
  249. df_between = len(results) - 1
  250. p_between = 1 - stats.chi2.cdf(Q_between, df_between)
  251. results["_subgroup_test"] = {
  252. "Q_between": float(Q_between),
  253. "df": df_between,
  254. "p_value": float(p_between),
  255. }
  257. return results
  260. # ---------------------------------------------------------------------------
  261. # Sensitivity analysis (leave-one-out)
  262. # ---------------------------------------------------------------------------
  264. def leave_one_out(
  265. effects: np.ndarray,
  266. variances: np.ndarray,
  267. labels: list[str],
  268. method: str = "random",
  269. ) -> list[dict]:
  270. """Leave-one-out sensitivity analysis."""
  271. effects = np.asarray(effects, dtype=float)
  272. variances = np.asarray(variances, dtype=float)
  273. results = []
  275. for i in range(len(effects)):
  276. mask = np.ones(len(effects), dtype=bool)
  277. mask[i] = False
  278. r = compute_pooled_effect(effects[mask], variances[mask], method)
  279. r["excluded_study"] = labels[i]
  280. results.append(r)
  282. return results
  285. # ---------------------------------------------------------------------------
  286. # Publication bias: Egger's test
  287. # ---------------------------------------------------------------------------
  289. def eggers_test(effects: np.ndarray, se: np.ndarray) -> dict:
  290. """
  291. Egger's regression test for funnel plot asymmetry.
  292. Regresses standardized effect (effect/SE) on precision (1/SE).
  293. """
  294. effects = np.asarray(effects, dtype=float)
  295. se = np.asarray(se, dtype=float)
  297. mask = (se > 0) & np.isfinite(effects) & np.isfinite(se)
  298. effects = effects[mask]
  299. se = se[mask]
  301. if len(effects) < 3:
  302. return {"error": "Need at least 3 studies for Egger's test"}
  304. precision = 1.0 / se
  305. standardized = effects / se
  307. slope, intercept, r_value, p_value, std_err = stats.linregress(precision, standardized)
  309. return {
  310. "intercept": float(intercept),
  311. "slope": float(slope),
  312. "se_intercept": float(std_err),
  313. "t_value": float(intercept / std_err) if std_err > 0 else float("nan"),
  314. "p_value": float(p_value),
  315. "significant": p_value < 0.05,
  316. "interpretation": "Significant asymmetry detected" if p_value < 0.05
  317. else "No significant asymmetry",
  318. }
  321. # ---------------------------------------------------------------------------
  322. # Plots
  323. # ---------------------------------------------------------------------------
  325. def forest_plot(
  326. df: pd.DataFrame,
  327. effect_col: str = "effect_size",
  328. ci_lower_col: str = "ci_lower",
  329. ci_upper_col: str = "ci_upper",
  330. label_col: str = "study_id",
  331. pooled: Optional[dict] = None,
  332. title: str = "Forest Plot",
  333. xlab: str = "Effect Size",
  334. null_value: float = 0,
  335. save_path: Optional[str] = None,
  336. ) -> plt.Figure:
  337. """Generate a forest plot from study data."""
  338. df = df.dropna(subset=[effect_col]).copy()
  339. if df.empty:
  340. fig, ax = plt.subplots()
  341. ax.text(0.5, 0.5, "No data available", ha="center", va="center")
  342. return fig
  344. fig, ax = plt.subplots(figsize=(11, max(4, len(df) * 0.45 + 2)))
  345. y_positions = range(len(df))
  347. ax.hlines(y_positions, df[ci_lower_col], df[ci_upper_col], color="black", lw=1)
  348. ax.scatter(df[effect_col], y_positions, zorder=5, color="steelblue", s=50)
  349. ax.axvline(null_value, color="grey", linestyle="--", lw=0.8)
  351. ax.set_yticks(list(y_positions))
  352. ax.set_yticklabels(df[label_col])
  353. ax.set_xlabel(xlab)
  354. ax.set_title(title, fontsize=11, fontweight="bold")
  356. x_min = float(np.nanmin(df[ci_lower_col].values))
  357. x_max = float(np.nanmax(df[ci_upper_col].values))
  358. diamond_y = len(df) + 0.5
  360. if pooled and "pooled_effect" in pooled:
  361. pe = pooled["pooled_effect"]
  362. ci_l = pooled.get("ci_lower", pe)
  363. ci_u = pooled.get("ci_upper", pe)
  364. x_min = min(x_min, ci_l)
  365. x_max = max(x_max, ci_u)
  367. ax.scatter(pe, diamond_y, marker="D", color="red", s=100, zorder=5)
  368. ax.hlines(diamond_y, ci_l, ci_u, color="red", lw=2)
  370. i2 = pooled.get("I2", 0)
  371. ax.text(
  372. ci_u + (ci_u - ci_l) * 0.1, diamond_y,
  373. f"Pooled: {pe:.2f} [{ci_l:.2f}, {ci_u:.2f}]\n"
  374. f"I²={i2:.1f}%, p={pooled.get('p_value', 0):.4f}",
  375. va="center", fontsize=8,
  376. )
  378. text_x = x_max + (x_max - x_min) * 0.08
  379. ax.text(text_x, -0.55, "Effect (95% CI)", ha="left", va="center", fontsize=8, fontweight="bold")
  380. for y, (_, row) in zip(y_positions, df.iterrows()):
  381. ax.text(
  382. text_x,
  383. y,
  384. f"{row[effect_col]:.2f} [{row[ci_lower_col]:.2f}, {row[ci_upper_col]:.2f}]",
  385. va="center",
  386. fontsize=8,
  387. )
  389. ax.set_xlim(left=max(0, x_min - (x_max - x_min) * 0.08), right=text_x + (x_max - x_min) * 0.35)
  390. ax.set_ylim(diamond_y + 0.8, -0.75)
  391. fig.tight_layout()
  393. if save_path:
  394. fig.savefig(save_path, dpi=300, bbox_inches="tight")
  395. logger.info("Forest plot saved to %s", save_path)
  397. return fig
  400. def forest_plot_hr(
  401. df: pd.DataFrame,
  402. hr_col: str = "hazard_ratio",
  403. hr_lower_col: str = "hr_ci_lower",
  404. hr_upper_col: str = "hr_ci_upper",
  405. label_col: str = "study_id",
  406. pooled: Optional[dict] = None,
  407. title: str = "Forest Plot — Hazard Ratios",
  408. save_path: Optional[str] = None,
  409. ) -> plt.Figure:
  410. """Forest plot for hazard ratios (log scale x-axis)."""
  411. df = df.dropna(subset=[hr_col]).copy()
  412. if df.empty:
  413. fig, ax = plt.subplots()
  414. ax.text(0.5, 0.5, "No HR data available", ha="center", va="center")
  415. return fig
  417. fig, ax = plt.subplots(figsize=(11, max(4, len(df) * 0.45 + 2)))
  418. y_positions = range(len(df))
  420. hrs = df[hr_col].values
  421. lowers = df[hr_lower_col].values if hr_lower_col in df.columns else hrs * 0.5
  422. uppers = df[hr_upper_col].values if hr_upper_col in df.columns else hrs * 2.0
  424. ax.hlines(y_positions, lowers, uppers, color="black", lw=1)
  425. ax.scatter(hrs, y_positions, zorder=5, color="steelblue", s=50)
  426. ax.axvline(1.0, color="grey", linestyle="--", lw=0.8)
  427. ax.set_xscale("log")
  429. ax.set_yticks(list(y_positions))
  430. ax.set_yticklabels(df[label_col])
  431. ax.set_xlabel("Hazard Ratio (log scale)")
  432. ax.set_title(title, fontsize=11, fontweight="bold")
  433. ax.set_xticks([0.5, 1, 2, 3, 5, 10])
  434. ax.set_xticklabels(["0.5", "1", "2", "3", "5", "10"])
  436. x_max = float(np.nanmax(uppers))
  437. diamond_y = len(df) + 0.5
  439. if pooled and "pooled_hr" in pooled:
  440. phr = pooled["pooled_hr"]
  441. ci_l = pooled.get("hr_ci_lower", phr)
  442. ci_u = pooled.get("hr_ci_upper", phr)
  443. x_max = max(x_max, ci_u)
  445. ax.scatter(phr, diamond_y, marker="D", color="red", s=100, zorder=5)
  446. ax.hlines(diamond_y, ci_l, ci_u, color="red", lw=2)
  448. i2 = pooled.get("I2", 0)
  449. ax.text(
  450. ci_u * 1.1, diamond_y,
  451. f"Pooled HR: {phr:.2f} [{ci_l:.2f}, {ci_u:.2f}]\n"
  452. f"I²={i2:.1f}%, p={pooled.get('p_value', 0):.4f}",
  453. va="center", fontsize=8,
  454. )
  456. text_x = x_max * 1.35
  457. ax.text(text_x, -0.55, "HR (95% CI)", va="center", fontsize=8, fontweight="bold")
  458. for y, hr, lo, hi in zip(y_positions, hrs, lowers, uppers):
  459. ax.text(text_x, y, f"{hr:.2f} [{lo:.2f}, {hi:.2f}]", va="center", fontsize=8)
  461. ax.set_xlim(left=max(0.4, float(np.nanmin(lowers)) * 0.8), right=text_x * 1.5)
  462. ax.set_ylim(diamond_y + 0.8, -0.75)
  463. fig.tight_layout()
  465. if save_path:
  466. fig.savefig(save_path, dpi=300, bbox_inches="tight")
  467. logger.info("HR forest plot saved to %s", save_path)
  469. return fig
  472. def funnel_plot(
  473. effects: np.ndarray,
  474. se: np.ndarray,
  475. title: str = "Funnel Plot",
  476. xlab: str = "Effect Size",
  477. egger: Optional[dict] = None,
  478. save_path: Optional[str] = None,
  479. ) -> plt.Figure:
  480. """Generate a funnel plot with optional Egger's test annotation."""
  481. effects = np.asarray(effects, dtype=float)
  482. se = np.asarray(se, dtype=float)
  484. fig, ax = plt.subplots(figsize=(8, 6))
  485. ax.scatter(effects, se, color="steelblue", alpha=0.7, edgecolors="black", lw=0.5)
  486. ax.set_xlabel(xlab)
  487. ax.set_ylabel("Standard Error")
  488. ax.invert_yaxis()
  490. mean_effect = np.nanmean(effects)
  491. ax.axvline(mean_effect, color="grey", linestyle="--", lw=0.8)
  493. # Pseudo-CI lines
  494. se_range = np.linspace(0.001, np.nanmax(se) * 1.1, 100)
  495. ax.plot(mean_effect - 1.96 * se_range, se_range, "k--", lw=0.5, alpha=0.5)
  496. ax.plot(mean_effect + 1.96 * se_range, se_range, "k--", lw=0.5, alpha=0.5)
  498. if egger:
  499. p = egger.get("p_value", 1)
  500. ax.text(
  501. 0.02, 0.98,
  502. f"Egger's test: p={p:.4f}\n{egger.get('interpretation', '')}",
  503. transform=ax.transAxes, va="top", fontsize=9,
  504. bbox=dict(boxstyle="round", facecolor="wheat", alpha=0.5),
  505. )
  507. fig.tight_layout()
  509. if save_path:
  510. fig.savefig(save_path, dpi=300, bbox_inches="tight")
  511. logger.info("Funnel plot saved to %s", save_path)
  513. return fig

meta.py, no license · at the source

Overview

Authors: Chase M. Walton1, Ben A. Strickland1
  1. Department of Neurosurgery, Medical University of South Carolina, Charleston, SC 29425, USA
Institutions: Medical University of South Carolina (United States)
Journal: Biomedicines, volume 14, issue 8, article 1702
Dates: received 26 June 2026; accepted 20 July 2026; published online 29 July 2026
Type: Review · Language: English
License: CC BY
Identifiers: DOI 10.3390/biomedicines14081702 · PMID 42652085 · PMCID PMC13510105 · OpenAlex W7171620422
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), other condition (population), clinical / translational (subfield)
Keywords: glioblastoma, complement system, immunotherapy, systematic review, tumor microenvironment
Topic: Complement system in diseases (Immunology, Immunology and Microbiology), according to OpenAlex
Funding: American Cancer Society Clinician Scientist Development (CSDG-25-1513999-01-IBCD)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Background/Objectives: Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor in adults, with a median survival of approximately 15 months despite maximal multimodal therapy. The complement system plays a paradoxical dual role in GBM, mediating both antitumor immunity and immunosuppressive signaling within the tumor microenvironment, yet no systematic synthesis of complement-targeted therapeutic strategies exists. We aimed to comprehensively identify, appraise, and synthesize studies investigating complement-targeted therapies and complement-associated prognosis in GBM. Methods: Following PRISMA 2020 guidelines, we searched PubMed/MEDLINE and the Cochrane Library (CENTRAL) without date or language restrictions. Preclinical and clinical study designs were eligible. Risk of bias was assessed using SYRCLE, ROBINS-I, and study-type-specific checklists. Certainty of evidence was evaluated using GRADE. Statistical pooling was planned only for sufficiently comparable studies; clinical prognostic studies were synthesized narratively because they assessed non-equivalent constructs. Results: Forty-one studies were included, comprising 15 preclinical in vivo, 13 preclinical in vitro, 6 clinical observational, and 7 bioinformatics studies. Five preclinical survival studies entered a structured quantitative synthesis, but no pooled cross-target effect was calculated because their interventions, comparators, and reported summary measures were non-equivalent. Clinical prognostic studies evaluated either individual protein biomarkers or multigene immune-risk signatures and were not pooled. GRADE certainty was “Very Low” for both outcomes. C3b opsonization and the C5a/C5aR1 axis were among the most frequently studied targets. Conclusions: Complement modulation remains a promising biological hypothesis in GBM rather than evidence for clinical application, and certainty of evidence is very low. Methodological and mechanistic heterogeneity across complement targets underscore the need for standardized preclinical models and randomized clinical trials.

Reproduced under the paper's license (CC BY), from the paper cited above.

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State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: Python (1)
Size: 11 files, 1 script
Software Heritage: not checked
Found in: “Supplementary Materials”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.17605/OSF.IO/W2FH3

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

All data supporting the findings of this systematic review are available within the article and its Supplementary Materials. The per-study extraction dataset, risk-of-bias assessments, and Python code for descriptive and exploratory analyses are provided as Supplementary Data and have been deposited in the Open Science Framework (OSF) repository (DOI: https://doi.org/10.17605/OSF.IO/W2FH3), which also archives the a priori protocol and the review’s retrospective registration.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 1 funder, 53 references.

Cite

This paper

Walton, C. M., & Strickland, B. A. (2026). Complement-Targeted Therapies in Glioblastoma: A Systematic Review. Biomedicines, 14(8), 1702. https://doi.org/10.3390/biomedicines14081702

BibTeX

@article{walton2026complement,
author = {Walton, Chase M. and Strickland, Ben A.},
title = {{Complement-Targeted Therapies in Glioblastoma: A Systematic Review}},
journal = {Biomedicines},
year = {2026},
month = jul,
volume = {14},
number = {8},
pages = {1702},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2227-9059},
doi = {10.3390/biomedicines14081702},
url = {https://doi.org/10.3390/biomedicines14081702},
pmid = {42652085},
pmcid = {PMC13510105}
}

RIS

TY - JOUR
AU - Walton, Chase M.
AU - Strickland, Ben A.
TI - Complement-Targeted Therapies in Glioblastoma: A Systematic Review
T2 - Biomedicines
J2 - Biomedicines
PY - 2026
DA - 2026/07/29
VL - 14
IS - 8
SP - 1702
SN - 2227-9059
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/biomedicines14081702
UR - https://doi.org/10.3390/biomedicines14081702
LA - en
ER -

CSL-JSON

{
"id": "10.3390/biomedicines14081702",
"type": "article-journal",
"title": "Complement-Targeted Therapies in Glioblastoma: A Systematic Review",
"container-title": "Biomedicines",
"author": [
{
"family": "Walton",
"given": "Chase M."
},
{
"family": "Strickland",
"given": "Ben A."
}
],
"container-title-short": "Biomedicines",
"volume": "14",
"issue": "8",
"page": "1702",
"DOI": "10.3390/biomedicines14081702",
"PMID": "42652085",
"PMCID": "PMC13510105",
"ISSN": "2227-9059",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/biomedicines14081702",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
29
]
]
}
}

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